David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties of methods we develop.
Some highlight application areas:
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc. Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.
(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.
(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease. This includes an emphasis on infectious disease modeling, such as COVID-19.
Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).
Some highlight application areas:
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc. Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.
(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.
(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease. This includes an emphasis on infectious disease modeling, such as COVID-19.
Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).
Current Research Interests
Bayesian methods for high-dimensional & complex data
Network data analysis
Scalable algorithms with provable guarantees
Methods for spatial & dynamic data
Statistical imaging
Interpretable machine learning & artificial intelligence
Applications in ecology/biodiversity, neuroscience & environmental health
Network data analysis
Scalable algorithms with provable guarantees
Methods for spatial & dynamic data
Statistical imaging
Interpretable machine learning & artificial intelligence
Applications in ecology/biodiversity, neuroscience & environmental health
Current Appointments & Affiliations
- Arts and Sciences Distinguished Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2013
- Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2008
- Professor in the Department of Mathematics, Mathematics, Trinity College of Arts & Sciences 2014
- Faculty Network Member of the Duke Institute for Brain Sciences, Duke Institute for Brain Sciences, University Institutes and Centers 2011
Contact Information
- 218 Old Chemistry Bldg, Durham, NC 27708
- Box 90251, Durham, NC 27708-0251
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dunson@duke.edu
(919) 684-8025
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Dunson CV
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GitHub repository - mostly code
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Google scholar profile
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NIPS 2018 Tutorial on Scalable Bayes
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papers on arXiv
- Background
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Education, Training, & Certifications
- Ph.D., Emory University 1997
- B.S., Pennsylvania State University 1994
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Previous Appointments & Affiliations
- Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2013 - 2018
- Recognition
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In the News
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OCT 11, 2019 -
FEB 20, 2017 -
FEB 15, 2017 -
JUN 1, 2015 Duke Research Blog -
JUN 1, 2015 Duke Research Blog -
APR 1, 2015 -
APR 1, 2015 The Washington Post -
APR 1, 2015 The Washington Post -
APR 1, 2015 The Guardian -
APR 1, 2015 The Guardian -
JUN 28, 2013 The Atlantic -
JUN 28, 2013 The Atlantic -
JUN 4, 2013
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Awards & Honors
- IMS Medallion Lecturer . Institute of Mathematical Statistics (IMS) . 2019
- David Finney Centenary Lecture. University of Edinburgh . May 2018
- Snedecor Lecture . Iowa State University . May 2018
- Carnegie Centenary Professorship . Carnegie Trust . 2018
- John A Lynch Lecturer . Notre Dame . 2018
- Mitchell Prize . International Society of Bayesian Analysis (ISBA). 2018
- van Dantzig lecturer. Netherlands . 2018
- Bradley Lecturer . University of Georgia . 2017
- DeGroot Prize . International Society of Bayesian Analysis . 2017
- JASA-T&M Editor-Selected Discussion Paper. Journal of the American Statistical Association . 2017
- W.J. Youden Award in Interlaboratory Testing. American Statistical Association. 2012
- COPSS Award: President's Award. American Statistical Association. 2010
- Fellow of the Institute of Mathematical Statistics (IMS). IMS. 2010
- Myrto Lefkopoulou Distinguished Lecture . Harvard University . 2010
- ASA Fellows. American Statistical Association. 2007
- Expertise
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Subject Headings
- Research
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Selected Grants
- Clinical and Genetic of Monomorphic Epitheliotropic Intestinal T Cell Lymphoma awarded by National Institutes of Health 2023 - 2027
- A Planetary Inventory of Life - a New Synthesis Built on Big Data Combined with Novel Statistical Methods awarded by European Research Council 2020 - 2026
- Science-Integrated Predictive modeLing (SCINPL): a novel framework for scalable and interpretable predictive scientific computing awarded by National Science Foundation 2022 - 2025
- Duke University Program in Environmental Health awarded by National Institutes of Health 2019 - 2024
- Calibrated uncertainty quantification in statistical learning awarded by Office of Naval Research 2021 - 2024
- HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation 2019 - 2023
- Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health 2017 - 2023
- Structured nonparametric methods for mixtures of exposures awarded by National Institutes of Health 2018 - 2023
- Reproducibility and Robustness of Dimensionality Reduction awarded by National Institutes of Health 2017 - 2022
- CRCNS: Geometry-based Brain Connectome Analysis awarded by National Institutes of Health 2018 - 2022
- An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency 2018 - 2022
- Probabilistic learning of structure in complex data awarded by Office of Naval Research 2017 - 2021
- Scalable probabilistic inference for huge multi-domain graphs awarded by Alibaba Innovative Research 2017 - 2021
- BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees awarded by National Science Foundation 2015 - 2020
- Predicting Performance from Network Data awarded by U.S. Army Research Institute for the Behavioral and Social Sciences 2016 - 2020
- New methods for quantitative modeling of protein-DNA interactions awarded by National Institutes of Health 2015 - 2020
- Network motifs in cortical computation awarded by University of California - Los Angeles 2016 - 2019
- Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health 2014 - 2019
- Air Quality by Genomics Interactions in a Cardiovascular Disease Cohort awarded by Health Effects Institute 2014 - 2017
- Bayesian learning for high-dimensional low sample size data awarded by Office of Naval Research 2014 - 2017
- LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data awarded by North Carolina State University 2016
- NCRN-MN:Triangle Census Research Network awarded by National Science Foundation 2011 - 2016
- Bayesian Methods for High-Dimensional Epidemiologic Data awarded by University of North Carolina - Chapel Hill 2011 - 2016
- Predicting Treatment Futility in Refractory Diffuse Large B cell Lymphoma awarded by Leukemia & Lymphoma Society 2014 - 2015
- Bayesian Methods for Assessing Gene by Environment Interactions awarded by National Institutes of Health 2009 - 2015
- Nonparametric Bayes Methods for Biomedical Studies awarded by National Institutes of Health 2009 - 2015
- Emergence of Cardiometabolic Risk Across the Lifecycle in China awarded by University of North Carolina - Chapel Hill 2013 - 2014
- Exome-wide screening for common mutations in lymphoma awarded by National Institutes of Health 2011 - 2013
- Transfer and Active Learning for Intent Recognition awarded by Office of Naval Research 2008 - 2012
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External Relationships
- Chapman Hall/CRC Press
- Merck Research Laboratories
- Publications & Artistic Works
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Selected Publications
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Books
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Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Bayesian data analysis, third edition, 2013.
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Academic Articles
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Legramanti, Sirio, Tommaso Rigon, Daniele Durante, and David B. Dunson. “EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS.” The Annals of Applied Statistics 16, no. 4 (December 2022): 2369–95. https://doi.org/10.1214/21-aoas1595.Full Text
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Chakraborty, Antik, Otso Ovaskainen, and David B. Dunson. “BAYESIAN SEMIPARAMETRIC LONG MEMORY MODELS FOR DISCRETIZED EVENT DATA.” The Annals of Applied Statistics 16, no. 3 (September 2022): 1380–99. https://doi.org/10.1214/21-aoas1546.Full Text
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Melikechi, Omar, Alexander L. Young, Tao Tang, Trevor Bowman, David Dunson, and James Johndrow. “Limits of epidemic prediction using SIR models.” Journal of Mathematical Biology 85, no. 4 (September 2022): 36. https://doi.org/10.1007/s00285-022-01804-5.Full Text
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Dey, Pritam, Zhengwu Zhang, and David B. Dunson. “Outlier detection for multi-network data.” Bioinformatics (Oxford, England) 38, no. 16 (August 2022): 4011–18. https://doi.org/10.1093/bioinformatics/btac431.Full Text
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Aliverti, Emanuele, and David B. Dunson. “COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES.” The Annals of Applied Statistics 16, no. 2 (June 2022): 765–90. https://doi.org/10.1214/21-aoas1515.Full Text
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Guha, S., R. Jung, and D. Dunson. “Predicting phenotypes from brain connection structure.” Journal of the Royal Statistical Society. Series C: Applied Statistics 71, no. 3 (June 1, 2022): 639–68. https://doi.org/10.1111/rssc.12549.Full Text
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Dunson, D. B., H. T. Wu, and N. Wu. “Graph based Gaussian processes on restricted domains.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 84, no. 2 (April 1, 2022): 414–39. https://doi.org/10.1111/rssb.12486.Full Text
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Lum, K., D. B. Dunson, and J. Johndrow. “Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment.” Journal of the Royal Statistical Society. Series A: Statistics in Society 185, no. 2 (April 1, 2022): 588–614. https://doi.org/10.1111/rssa.12792.Full Text
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Russo, Massimiliano, Burton H. Singer, and David B. Dunson. “MULTIVARIATE MIXED MEMBERSHIP MODELING: INFERRING DOMAIN-SPECIFIC RISK PROFILES.” The Annals of Applied Statistics 16, no. 1 (March 2022): 391–413. https://doi.org/10.1214/21-aoas1496.Full Text
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Van Den Boom, W., G. Reeves, and D. B. Dunson. “Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068).” Biometrika 109, no. 1 (March 1, 2022): 275. https://doi.org/10.1093/biomet/asab019.Full Text
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Badea, Alexandra, Didong Li, Andrei R. Niculescu, Robert J. Anderson, Jacques A. Stout, Christina L. Williams, Carol A. Colton, Nobuyo Maeda, and David B. Dunson. “Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer's Disease.” Front Neurosci 16 (2022): 848654. https://doi.org/10.3389/fnins.2022.848654.Full Text Link to Item
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Badea, Alexandra, Didong Li, Andrei R. Niculescu, Robert J. Anderson, Jacques A. Stout, Christina L. Williams, Carol A. Colton, Nobuyo Maeda, and David B. Dunson. “Corrigendum: Absolute winding number differentiates mouse spatial navigation strategies with genetic risk for Alzheimer's disease.” Front Neurosci 16 (2022): 1070425. https://doi.org/10.3389/fnins.2022.1070425.Full Text Link to Item
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Joubert, Bonnie R., Marianthi-Anna Kioumourtzoglou, Toccara Chamberlain, Hua Yun Chen, Chris Gennings, Mary E. Turyk, Marie Lynn Miranda, et al. “Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.” International Journal of Environmental Research and Public Health 19, no. 3 (January 2022): 1378. https://doi.org/10.3390/ijerph19031378.Full Text
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Peruzzi, Michele, and David B. Dunson. “Spatial Multivariate Trees for Big Data Bayesian Regression.” Journal of Machine Learning Research : Jmlr 23 (January 2022): 17.
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Zhang, R., S. Mak, and D. Dunson. “GAUSSIAN PROCESS SUBSPACE PREDICTION FOR MODEL REDUCTION.” Siam Journal on Scientific Computing 44, no. 3 (January 1, 2022): A1428–49. https://doi.org/10.1137/21M1432739.Full Text
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Zito, A., T. Rigon, O. Ovaskainen, and D. B. Dunson. “Bayesian Modeling of Sequential Discoveries.” Journal of the American Statistical Association, January 1, 2022. https://doi.org/10.1080/01621459.2022.2060835.Full Text
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Zito, A., T. Rigon, and D. B. Dunson. “Inferring taxonomic placement from DNA barcoding aiding in discovery of new taxa.” Methods in Ecology and Evolution, January 1, 2022. https://doi.org/10.1111/2041-210X.14009.Full Text
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Liu, Meimei, Zhengwu Zhang, and David B. Dunson. “Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets.” Neuroimage 245 (December 2021): 118750. https://doi.org/10.1016/j.neuroimage.2021.118750.Full Text
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Dunson, D. B., H. T. Wu, and N. Wu. “Spectral convergence of graph Laplacian and heat kernel reconstruction in L∞ from random samples.” Applied and Computational Harmonic Analysis 55 (November 1, 2021): 282–336. https://doi.org/10.1016/j.acha.2021.06.002.Full Text
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Moran, Kelly R., David Dunson, Matthew W. Wheeler, and Amy H. Herring. “BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES.” The Annals of Applied Statistics 15, no. 3 (September 2021): 1405–30. https://doi.org/10.1214/21-aoas1461.Full Text
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Roy, Arkaprava, Isaac Lavine, Amy H. Herring, and David B. Dunson. “PERTURBED FACTOR ANALYSIS: ACCOUNTING FOR GROUP DIFFERENCES IN EXPOSURE PROFILES.” The Annals of Applied Statistics 15, no. 3 (September 2021): 1386–1404. https://doi.org/10.1214/20-aoas1435.Full Text
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Aliverti, Emanuele, Kristian Lum, James E. Johndrow, and David B. Dunson. “Removing the influence of group variables in high-dimensional predictive modelling.” Journal of the Royal Statistical Society. Series A, (Statistics in Society) 184, no. 3 (July 2021): 791–811. https://doi.org/10.1111/rssa.12613.Full Text
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Moran, Kelly R., Elizabeth L. Turner, David Dunson, and Amy H. Herring. “Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.” J R Stat Soc Ser C Appl Stat 70, no. 3 (June 2021): 532–57. https://doi.org/10.1111/rssc.12468.Full Text Link to Item
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VAN DEN Boom, W., G. Reeves, and D. B. Dunson. “Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation.” Biometrika 108, no. 2 (June 2021): 269–82. https://doi.org/10.1093/biomet/asaa068.Full Text
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Paganin, Sally, Amy H. Herring, Andrew F. Olshan, David B. Dunson, and David B. National Birth Defects Prevention Study. “Centered Partition Processes: Informative Priors for Clustering (with Discussion).” Bayesian Analysis 16, no. 1 (March 2021): 301–70. https://doi.org/10.1214/20-ba1197.Full Text
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Duan, Leo L., and David B. Dunson. “Bayesian Distance Clustering.” Journal of Machine Learning Research : Jmlr 22 (January 2021): 224.
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Ferrari, Federico, and David B. Dunson. “Bayesian Factor Analysis for Inference on Interactions.” Journal of the American Statistical Association 116, no. 535 (January 2021): 1521–32. https://doi.org/10.1080/01621459.2020.1745813.Full Text
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Jauch, M., P. D. Hoff, and D. B. Dunson. “Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion.” Journal of Computational and Graphical Statistics 30, no. 3 (January 1, 2021): 622–31. https://doi.org/10.1080/10618600.2020.1859382.Full Text
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Lee, K., L. Lin, and D. Dunson. “Maximum pairwise bayes factors for covariance structure testing.” Electronic Journal of Statistics 15, no. 2 (January 1, 2021): 4384–4419. https://doi.org/10.1214/21-EJS1900.Full Text
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Papadogeorgou, G., Z. Zhang, and D. B. Dunson. “Soft tensor regression.” Journal of Machine Learning Research 22 (January 1, 2021): 1–53.
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Roy, Arkaprava, Jana Schaich Borg, and David B. Dunson. “Bayesian time-aligned factor analysis of paired multivariate time series.” Journal of Machine Learning Research : Jmlr 22 (January 2021): 250.
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Zhu, Y., C. Li, and D. B. Dunson. “Classification Trees for Imbalanced Data: Surface-to-Volume Regularization.” Journal of the American Statistical Association, January 1, 2021. https://doi.org/10.1080/01621459.2021.2005609.Full Text
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Sen, Deborshee, Matthias Sachs, Jianfeng Lu, and David B. Dunson. “Efficient posterior sampling for high-dimensional imbalanced logistic regression.” Biometrika 107, no. 4 (December 2020): 1005–12. https://doi.org/10.1093/biomet/asaa035.Full Text
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Ferrari, Federico, and David B. Dunson. “IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES.” The Annals of Applied Statistics 14, no. 4 (December 2020): 1743–58. https://doi.org/10.1214/20-aoas1363.Full Text
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Mukhopadhyay, Minerva, Didong Li, and David B. Dunson. “Estimating densities with non-linear support by using Fisher-Gaussian kernels.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 82, no. 5 (December 2020): 1249–71. https://doi.org/10.1111/rssb.12390.Full Text
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Roy, Arkaprava, and David B. Dunson. “Nonparametric graphical model for counts.” Journal of Machine Learning Research : Jmlr 21 (December 2020): 229.
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Legramanti, Sirio, Daniele Durante, and David B. Dunson. “Bayesian cumulative shrinkage for infinite factorizations.” Biometrika 107, no. 3 (September 2020): 745–52. https://doi.org/10.1093/biomet/asaa008.Full Text
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Dunson, D., and T. Papamarkou. “Discussions.” International Statistical Review 88, no. 2 (August 1, 2020): 321–24. https://doi.org/10.1111/insr.12375.Full Text
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Binette, O., D. Pati, and D. B. Dunson. “Bayesian closed surface fitting through tensor products.” Journal of Machine Learning Research 21 (July 1, 2020): 1–26.
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Aliverti, Emanuele, Jeffrey L. Tilson, Dayne L. Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R. Gershon, Kirk C. Wilhelmsen, and David B. Dunson. “Projected t-SNE for batch correction.” Bioinformatics (Oxford, England) 36, no. 11 (June 2020): 3522–27. https://doi.org/10.1093/bioinformatics/btaa189.Full Text
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Nishimura, A., D. B. Dunson, and J. Lu. “Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.” Biometrika 107, no. 2 (June 1, 2020): 365–80. https://doi.org/10.1093/biomet/asz083.Full Text
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Duan, Leo L., Alexander L. Young, Akihiko Nishimura, and David B. Dunson. “Bayesian constraint relaxation.” Biometrika 107, no. 1 (March 2020): 191–204. https://doi.org/10.1093/biomet/asz069.Full Text
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Dunson, D. B., and J. E. Johndrow. “The Hastings algorithm at fifty.” Biometrika 107, no. 1 (March 1, 2020): 1–23. https://doi.org/10.1093/biomet/asz066.Full Text
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Tikhonov, Gleb, Li Duan, Nerea Abrego, Graeme Newell, Matt White, David Dunson, and Otso Ovaskainen. “Computationally efficient joint species distribution modeling of big spatial data.” Ecology 101, no. 2 (February 2020): e02929. https://doi.org/10.1002/ecy.2929.Full Text
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Jauch, M., P. D. Hoff, and D. B. Dunson. “Random orthogonal matrices and the Cayley transform.” Bernoulli 26, no. 2 (January 1, 2020): 1560–86. https://doi.org/10.3150/19-BEJ1176.Full Text
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Li, Meng, and David B. Dunson. “Comparing and weighting imperfect models using D-probabilities.” Journal of the American Statistical Association 115, no. 531 (January 2020): 1349–60. https://doi.org/10.1080/01621459.2019.1611140.Full Text
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Mukhopadhyay, M., and D. B. Dunson. “Targeted Random Projection for Prediction From High-Dimensional Features.” Journal of the American Statistical Association 115, no. 532 (January 1, 2020): 1998–2010. https://doi.org/10.1080/01621459.2019.1677240.Full Text
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Nishimura, A., and D. Dunson. “Recycling Intermediate Steps to Improve Hamiltonian Monte Carlo.” Bayesian Analysis 15, no. 4 (January 1, 2020): 1087–1108. https://doi.org/10.1214/19-BA1171.Full Text
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Talbot, Austin, David Dunson, Kafui Dzirasa, and David Carlson. “Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.” Arxiv Preprint Arxiv:2004.05209, 2020.Open Access Copy
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Camerlenghi, F., D. B. Dunson, A. Lijoi, I. Prunster, and A. Rodríguez. “Latent nested nonparametric priors (with discussion).” Bayesian Analysis 14, no. 4 (December 1, 2019): 1303–56. https://doi.org/10.1214/19-BA1169_1.Full Text
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Camerlenghi, Federico, David B. Dunson, Antonio Lijoi, Igor Prünster, and Abel Rodríguez. “Latent Nested Nonparametric Priors (with Discussion).” Bayesian Analysis 14, no. 4 (December 2019): 1303–56. https://doi.org/10.1214/19-ba1169.Full Text
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Panea, Razvan I., Cassandra L. Love, Jennifer R. Shingleton, Anupama Reddy, Jeffrey A. Bailey, Ann M. Moormann, Juliana A. Otieno, et al. “The whole-genome landscape of Burkitt lymphoma subtypes.” Blood 134, no. 19 (November 7, 2019): 1598–1607. https://doi.org/10.1182/blood.2019001880.Full Text Link to Item
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Chae, M., L. Lin, and D. B. Dunson. “Bayesian sparse linear regression with unknown symmetric error.” Information and Inference 8, no. 3 (September 19, 2019): 621–53. https://doi.org/10.1093/imaiai/iay022.Full Text
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Norberg, A., N. Abrego, F. G. Blanchet, F. R. Adler, B. J. Anderson, J. Anttila, M. B. Araújo, et al. “A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels.” Ecological Monographs 89, no. 3 (August 1, 2019). https://doi.org/10.1002/ecm.1370.Full Text
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Zhang, Zhengwu, Genevera I. Allen, Hongtu Zhu, and David Dunson. “Tensor network factorizations: Relationships between brain structural connectomes and traits.” Neuroimage 197 (August 2019): 330–43. https://doi.org/10.1016/j.neuroimage.2019.04.027.Full Text
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Li, Cheng, Lizhen Lin, and David B. Dunson. “On posterior consistency of tail index for Bayesian kernel mixture models.” Bernoulli 25, no. 3 (August 1, 2019). https://doi.org/10.3150/18-bej1043.Full Text
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Johndrow, J. E., A. Smith, N. Pillai, and D. B. Dunson. “MCMC for Imbalanced Categorical Data.” Journal of the American Statistical Association 114, no. 527 (July 3, 2019): 1394–1403. https://doi.org/10.1080/01621459.2018.1505626.Full Text
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Niu, M., P. Cheung, L. Lin, Z. Dai, N. Lawrence, and D. Dunson. “Intrinsic Gaussian processes on complex constrained domains.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 81, no. 3 (July 1, 2019): 603–27. https://doi.org/10.1111/rssb.12320.Full Text
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Wang, L., Z. Zhang, and D. Dunson. “Symmetric Bilinear Regression for Signal Subgraph Estimation.” Ieee Transactions on Signal Processing 67, no. 7 (April 1, 2019): 1929–40. https://doi.org/10.1109/TSP.2019.2899818.Full Text
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Lin, L., N. Mu, P. Cheung, and D. Dunson. “Extrinsic Gaussian processes for regression and classification on manifolds.” Bayesian Analysis 14, no. 3 (January 1, 2019): 887–906. https://doi.org/10.1214/18-BA1135.Full Text
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Miller, Jeffrey W., and David B. Dunson. “Robust Bayesian inference via coarsening.” Journal of the American Statistical Association 114, no. 527 (January 2019): 1113–25. https://doi.org/10.1080/01621459.2018.1469995.Full Text
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Zhang, Zhengwu, Maxime Descoteaux, and David B. Dunson. “Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions.” Journal of the American Statistical Association 114, no. 528 (January 2019): 1505–17. https://doi.org/10.1080/01621459.2019.1574582.Full Text
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Badea, Alexandra, Wenlin Wu, Jordan Shuff, Michele Wang, Robert J. Anderson, Yi Qi, G Allan Johnson, et al. “Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease.” Front Neuroinform 13 (2019): 72. https://doi.org/10.3389/fninf.2019.00072.Full Text Link to Item
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Canale, Antonio, Daniele Durante, and David B. Dunson. “Convex mixture regression for quantitative risk assessment.” Biometrics 74, no. 4 (December 2018): 1331–40. https://doi.org/10.1111/biom.12917.Full Text
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Sarkar, A., J. Chabout, J. J. Macopson, E. D. Jarvis, and D. B. Dunson. “Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax.” Journal of the American Statistical Association 113, no. 524 (October 2, 2018): 1515–27. https://doi.org/10.1080/01621459.2018.1423986.Full Text
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Duan, L. L., J. E. Johndrow, and D. B. Dunson. “Scaling up data augmentation MCMC via calibration.” Journal of Machine Learning Research 19 (October 1, 2018).
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Srivastava, S., C. Li, and D. B. Dunson. “Scalable Bayes via barycenter in Wasserstein space.” Journal of Machine Learning Research 19 (August 1, 2018): 1–35.
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Boom, Willem van den, Callie Mao, Rebecca A. Schroeder, and David B. Dunson. “Extrema-weighted feature extraction for functional data.” Bioinformatics 34, no. 14 (July 15, 2018): 2457–64. https://doi.org/10.1093/bioinformatics/bty120.Full Text Link to Item
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Guhaniyogi, R., S. Qamar, and D. B. Dunson. “Bayesian Conditional Density Filtering.” Journal of Computational and Graphical Statistics 27, no. 3 (July 3, 2018): 657–72. https://doi.org/10.1080/10618600.2017.1422431.Full Text
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Shterev, Ivo D., David B. Dunson, Cliburn Chan, and Gregory D. Sempowski. “Bayesian Multi-Plate High-Throughput Screening of Compounds.” Sci Rep 8, no. 1 (June 22, 2018): 9551. https://doi.org/10.1038/s41598-018-27531-w.Full Text Link to Item
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Johndrow, J. E., K. Lum, and D. B. Dunson. “Theoretical limits of microclustering for record linkage.” Biometrika 105, no. 2 (June 2018): 431–46. https://doi.org/10.1093/biomet/asy003.Full Text
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Dunson, D. B. “Statistics in the big data era: Failures of the machine.” Statistics and Probability Letters 136 (May 1, 2018): 4–9. https://doi.org/10.1016/j.spl.2018.02.028.Full Text
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Zhang, Zhengwu, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David Dunson, Anuj Srivastava, and Hongtu Zhu. “Mapping population-based structural connectomes.” Neuroimage 172 (May 2018): 130–45. https://doi.org/10.1016/j.neuroimage.2017.12.064.Full Text
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Bertrán, Martín A., Natalia L. Martínez, Ye Wang, David Dunson, Guillermo Sapiro, and Dario Ringach. “Active learning of cortical connectivity from two-photon imaging data.” Plos One 13, no. 5 (January 2018): e0196527. https://doi.org/10.1371/journal.pone.0196527.Full Text
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Durante, D., and D. B. Dunson. “Bayesian inference and testing of group differences in brain networks.” Bayesian Analysis 13, no. 1 (January 1, 2018): 29–58. https://doi.org/10.1214/16-BA1030.Full Text
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Durante, D., and D. B. Dunson. “Supplementary Material For “Bayesian Inference And Testing Of Group Differences In Brain Networks”.” Bayesian Analysis 13, no. 1 (January 1, 2018): 1–2. https://doi.org/10.1214/16-BA1030SUPP.Full Text
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Zhao, Shiwen, Barbara E. Engelhardt, Sayan Mukherjee, and David B. Dunson. “Fast Moment Estimation for Generalized Latent Dirichlet Models.” Journal of the American Statistical Association 113, no. 524 (January 2018): 1528–40. https://doi.org/10.1080/01621459.2017.1341839.Full Text
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Minsker, S., S. Srivastava, L. Lin, and D. B. Dunson. “Robust and scalable bayes via a median of subset posterior measures.” Journal of Machine Learning Research 18 (December 1, 2017): 1–40.
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Wheeler, M. W., D. B. Dunson, and A. H. Herring. “Bayesian local extremum splines.” Biometrika 104, no. 4 (December 1, 2017): 939–52.
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Wheeler, M. W., D. B. Dunson, and A. H. Herring. “Bayesian local extremum splines.” Biometrika 104, no. 4 (December 1, 2017): 939–52. https://doi.org/10.1093/biomet/asx039.Full Text Link to Item
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Shang, Y., D. Dunson, and J. S. Song. “Exploiting big data in logistics risk assessment via Bayesian nonparametrics.” Operations Research 65, no. 6 (November 1, 2017): 1574–88. https://doi.org/10.1287/opre.2017.1612.Full Text
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Reddy, Anupama, Jenny Zhang, Nicholas S. Davis, Andrea B. Moffitt, Cassandra L. Love, Alexander Waldrop, Sirpa Leppa, et al. “Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma.” Cell 171, no. 2 (October 5, 2017): 481-494.e15. https://doi.org/10.1016/j.cell.2017.09.027.Full Text Link to Item
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Durante, D., D. B. Dunson, and J. T. Vogelstein. “Nonparametric Bayes Modeling of Populations of Networks.” Journal of the American Statistical Association 112, no. 520 (October 2, 2017): 1516–30. https://doi.org/10.1080/01621459.2016.1219260.Full Text Open Access Copy
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Durante, D., D. B. Dunson, and J. T. Vogelstein. “Rejoinder: Nonparametric Bayes Modeling of Populations of Networks.” Journal of the American Statistical Association 112, no. 520 (October 2, 2017): 1547–52. https://doi.org/10.1080/01621459.2017.1395643.Full Text
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Li, C., S. Srivastava, and D. B. Dunson. “Simple, scalable and accurate posterior interval estimation.” Biometrika 104, no. 3 (September 1, 2017): 665–80. https://doi.org/10.1093/biomet/asx033.Full Text
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Lock, Eric F., and David B. Dunson. “Bayesian genome- and epigenome-wide association studies with gene level dependence.” Biometrics 73, no. 3 (September 2017): 1018–28. https://doi.org/10.1111/biom.12649.Full Text
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Srivastava, Sanvesh, Barbara E. Engelhardt, and David B. Dunson. “Expandable factor analysis.” Biometrika 104, no. 3 (September 2017): 649–63. https://doi.org/10.1093/biomet/asx030.Full Text
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Guhaniyogi, R., S. Qamar, and D. B. Dunson. “Bayesian tensor regression.” Journal of Machine Learning Research 18 (August 1, 2017): 1–31.
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Rao, V., R. P. Adams, and D. D. Dunson. “Bayesian inference for Matérn repulsive processes.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 79, no. 3 (June 1, 2017): 877–97. https://doi.org/10.1111/rssb.12198.Full Text
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Wang, Lu, Daniele Durante, Rex E. Jung, and David B. Dunson. “Bayesian network-response regression.” Bioinformatics (Oxford, England) 33, no. 12 (June 2017): 1859–66. https://doi.org/10.1093/bioinformatics/btx050.Full Text Open Access Copy
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Zhu, B., and D. B. Dunson. “Bayesian functional data modeling for heterogeneous volatility.” Bayesian Analysis 12, no. 2 (June 1, 2017): 335–50. https://doi.org/10.1214/16-BA1004.Full Text
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Schaich Borg, Jana, Sanvesh Srivastava, Lizhen Lin, Joseph Heffner, David Dunson, Kafui Dzirasa, and Luis de Lecea. “Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.” Brain Behav 7, no. 6 (June 2017): e00710. https://doi.org/10.1002/brb3.710.Full Text Open Access Copy Link to Item
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Ovaskainen, Otso, Gleb Tikhonov, Anna Norberg, F. Guillaume Blanchet, Leo Duan, David Dunson, Tomas Roslin, and Nerea Abrego. “How to make more out of community data? A conceptual framework and its implementation as models and software.” Ecology Letters 20, no. 5 (May 2017): 561–76. https://doi.org/10.1111/ele.12757.Full Text
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Moffitt, Andrea B., Sarah L. Ondrejka, Matthew McKinney, Rachel E. Rempel, John R. Goodlad, Chun Huat Teh, Sirpa Leppa, et al. “Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2.” J Exp Med 214, no. 5 (May 1, 2017): 1371–86. https://doi.org/10.1084/jem.20160894.Full Text Link to Item
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Ovaskainen, Otso, Gleb Tikhonov, David Dunson, Vidar Grøtan, Steinar Engen, Bernt-Erik Sæther, and Nerea Abrego. “How are species interactions structured in species-rich communities? A new method for analysing time-series data.” Proceedings. Biological Sciences 284, no. 1855 (May 2017): 20170768. https://doi.org/10.1098/rspb.2017.0768.Full Text
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Durante, D., S. Paganin, B. Scarpa, and D. B. Dunson. “Bayesian modelling of networks in complex business intelligence problems.” Journal of the Royal Statistical Society. Series C: Applied Statistics 66, no. 3 (April 1, 2017): 555–80. https://doi.org/10.1111/rssc.12168.Full Text
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Lin, L., V. Rao, and D. Dunson. “Bayesian nonparametric inference on the stiefel manifold.” Statistica Sinica 27, no. 2 (April 1, 2017): 535–53. https://doi.org/10.5705/ss.202016.0017.Full Text
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Tikhonov, G., N. Abrego, D. Dunson, and O. Ovaskainen. “Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context.” Methods in Ecology and Evolution 8, no. 4 (April 1, 2017): 443–52. https://doi.org/10.1111/2041-210X.12723.Full Text
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McKinney, Matthew, Andrea B. Moffitt, Philippe Gaulard, Marion Travert, Laurence De Leval, Alina Nicolae, Mark Raffeld, et al. “The Genetic Basis of Hepatosplenic T-cell Lymphoma.” Cancer Discov 7, no. 4 (April 2017): 369–79. https://doi.org/10.1158/2159-8290.CD-16-0330.Full Text Link to Item
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Abrego, N., D. Dunson, P. Halme, I. Salcedo, and O. Ovaskainen. “Wood-inhabiting fungi with tight associations with other species have declined as a response to forest management.” Oikos 126, no. 2 (February 1, 2017). https://doi.org/10.1111/oik.03674.Full Text
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Dunson, D. B. “Toward automated prior choice.” Statistical Science 32, no. 1 (February 1, 2017): 41–43. https://doi.org/10.1214/16-STS607.Full Text
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Dunson, D., and P. Fryzlewicz. “Report of the editors-2016.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 79, no. 1 (January 1, 2017): 3–4. https://doi.org/10.1111/rssb.12220.Full Text
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Johndrow, James E., Anirban Bhattacharya, and David B. Dunson. “TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS.” Annals of Statistics 45, no. 1 (January 2017): 1–38. https://doi.org/10.1214/15-aos1414.Full Text
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Lin, Lizhen, Brian St Thomas, Hongtu Zhu, and David B. Dunson. “Extrinsic local regression on manifold-valued data.” Journal of the American Statistical Association 112, no. 519 (January 2017): 1261–73. https://doi.org/10.1080/01621459.2016.1208615.Full Text
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Bhattacharya, A., D. B. Dunson, D. Pati, and N. S. Pillai. “Sub-optimality of some continuous shrinkage priors.” Stochastic Processes and Their Applications 126, no. 12 (December 1, 2016): 3828–42. https://doi.org/10.1016/j.spa.2016.08.007.Full Text
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Durante, D., and D. B. Dunson. “Locally adaptive dynamic networks.” Annals of Applied Statistics 10, no. 4 (December 1, 2016): 2203–32. https://doi.org/10.1214/16-AOAS971.Full Text
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Datta, Jyotishka, and David B. Dunson. “Bayesian inference on quasi-sparse count data.” Biometrika 103, no. 4 (December 2016): 971–83. https://doi.org/10.1093/biomet/asw053.Full Text
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Sarkar, A., and D. B. Dunson. “Bayesian Nonparametric Modeling of Higher Order Markov Chains.” Journal of the American Statistical Association 111, no. 516 (October 1, 2016): 1791–1803. https://doi.org/10.1080/01621459.2015.1115763.Full Text
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Zhu, H., N. Strawn, and D. B. Dunson. “Bayesian graphical models for multivariate functional data.” Journal of Machine Learning Research 17 (October 1, 2016): 1–27.
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Yin, R., B. Cornelis, G. Fodor, N. Ocon, D. Dunson, and I. Daubechies. “Removing cradle artifacts in X-ray images of paintings.” Siam Journal on Imaging Sciences 9, no. 3 (August 30, 2016): 1247–72. https://doi.org/10.1137/15M1053554.Full Text
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Li, Daniel, Leslie Heyer, Victoria H. Jennings, Colin A. Smith, and David B. Dunson. “Personalised estimation of a woman's most fertile days.” The European Journal of Contraception & Reproductive Health Care : The Official Journal of the European Society of Contraception 21, no. 4 (August 2016): 323–28. https://doi.org/10.1080/13625187.2016.1196485.Full Text
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Hultman, Rainbo, Stephen D. Mague, Qiang Li, Brittany M. Katz, Nadine Michel, Lizhen Lin, Joyce Wang, et al. “Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology.” Neuron 91, no. 2 (July 20, 2016): 439–52. https://doi.org/10.1016/j.neuron.2016.05.038.Full Text Link to Item
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Canale, A., and D. B. Dunson. “Multiscale bernstein polynomials for densities.” Statistica Sinica 26, no. 3 (July 1, 2016): 1175–95. https://doi.org/10.5705/ss.202015.0163.Full Text
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Kunihama, T., A. H. Herring, C. T. Halpern, and D. B. Dunson. “Nonparametric Bayes modeling with sample survey weights.” Statistics & Probability Letters 113 (June 2016): 41–48. https://doi.org/10.1016/j.spl.2016.02.009.Full Text
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Rao, Vinayak, Lizhen Lin, and David B. Dunson. “Data augmentation for models based on rejection sampling.” Biometrika 103, no. 2 (June 2016): 319–35. https://doi.org/10.1093/biomet/asw005.Full Text Open Access Copy
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Guhaniyogi, R., and D. B. Dunson. “Compressed Gaussian process for manifold regression.” Journal of Machine Learning Research 17 (May 1, 2016).
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Ovaskainen, O., N. Abrego, P. Halme, and D. Dunson. “Using latent variable models to identify large networks of species-to-species associations at different spatial scales.” Methods in Ecology and Evolution 7, no. 5 (May 1, 2016): 549–55. https://doi.org/10.1111/2041-210X.12501.Full Text
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Kabisa, S., D. B. Dunson, and J. S. Morris. “Online Variational Bayes Inference for High-Dimensional Correlated Data.” Journal of Computational and Graphical Statistics 25, no. 2 (April 2, 2016): 426–44. https://doi.org/10.1080/10618600.2014.998336.Full Text
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Yang, Y., and D. B. Dunson. “Bayesian manifold regression.” Annals of Statistics 44, no. 2 (April 1, 2016): 876–905. https://doi.org/10.1214/15-AOS1390.Full Text
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Tang, Kewei, David B. Dunson, Zhixun Su, Risheng Liu, Jie Zhang, and Jiangxin Dong. “Subspace segmentation by dense block and sparse representation.” Neural Networks : The Official Journal of the International Neural Network Society 75 (March 2016): 66–76. https://doi.org/10.1016/j.neunet.2015.11.011.Full Text
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Zhou, Jing, Amy H. Herring, Anirban Bhattacharya, Andrew F. Olshan, David B. Dunson, and David B. National Birth Defects Prevention Study. “Nonparametric Bayes modeling for case control studies with many predictors.” Biometrics 72, no. 1 (March 2016): 184–92. https://doi.org/10.1111/biom.12411.Full Text
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Chabout, Jonathan, Abhra Sarkar, Sheel R. Patel, Taylor Radden, David B. Dunson, Simon E. Fisher, and Erich D. Jarvis. “A Foxp2 Mutation Implicated in Human Speech Deficits Alters Sequencing of Ultrasonic Vocalizations in Adult Male Mice.” Front Behav Neurosci 10 (2016): 197. https://doi.org/10.3389/fnbeh.2016.00197.Full Text Open Access Copy Link to Item
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Yang, Yun, and David B. Dunson. “Bayesian Conditional Tensor Factorizations for High-Dimensional Classification.” Journal of the American Statistical Association 111, no. 514 (January 2016): 656–69. https://doi.org/10.1080/01621459.2015.1029129.Full Text
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Bhattacharya, Anirban, Debdeep Pati, Natesh S. Pillai, and David B. Dunson. “Dirichlet-Laplace priors for optimal shrinkage.” Journal of the American Statistical Association 110, no. 512 (December 2015): 1479–90. https://doi.org/10.1080/01621459.2014.960967.Full Text
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Fox, E. B., D. B. Dunson, and E. M. Airoldi. “Bayesian nonparametric covariance regression.” Journal of Machine Learning Research 16 (December 1, 2015): 2501–42.
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Lock, Eric F., and David B. Dunson. “Shared kernel Bayesian screening.” Biometrika 102, no. 4 (December 2015): 829–42. https://doi.org/10.1093/biomet/asv032.Full Text
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Yazdani, A., and D. B. Dunson. “A hybrid bayesian approach for genome-wide association studies on related individuals.” Bioinformatics (Oxford, England) 31, no. 24 (December 2015): 3890–96. https://doi.org/10.1093/bioinformatics/btv496.Full Text
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Guhaniyogi, R., and D. B. Dunson. “Bayesian Compressed Regression.” Journal of the American Statistical Association 110, no. 512 (October 2, 2015): 1500–1514. https://doi.org/10.1080/01621459.2014.969425.Full Text
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Johndrow, James E., Jonathan C. Mattingly, Sayan Mukherjee, and David Dunson. “Optimal approximating Markov chains for Bayesian inference,” August 13, 2015.Open Access Copy Link to Item
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Hua, Zhaowei, Hongtu Zhu, and David B. Dunson. “Semiparametric Bayes local additive models for longitudinal data.” Statistics in Biosciences 7, no. 1 (May 2015): 90–107. https://doi.org/10.1007/s12561-013-9104-y.Full Text
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Chabout, Jonathan, Abhra Sarkar, David B. Dunson, and Erich D. Jarvis. “Male mice song syntax depends on social contexts and influences female preferences.” Frontiers in Behavioral Neuroscience 9 (April 1, 2015). https://doi.org/10.3389/fnbh.2015.00076.Full Text Open Access Copy Link to Item
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Li, Daniel, Allen J. Wilcox, and David B. Dunson. “Benchmark pregnancy rates and the assessment of post-coital contraceptives: an update.” Contraception 91, no. 4 (April 2015): 344–49. https://doi.org/10.1016/j.contraception.2015.01.002.Full Text
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Strawn, N., A. Armagan, R. Saab, L. Carin, and D. Dunson. “Erratum: Finite sample posterior concentration in high-dimensional regression (Information and Inference (2015) 3 (103-133) DOI: 10.1093/imaiai/iau003).” Information and Inference 4, no. 1 (March 1, 2015): 77. https://doi.org/10.1093/imaiai/iau008.Full Text
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Lock, Eric F., Karen L. Soldano, Melanie E. Garrett, Heidi Cope, Christina A. Markunas, Herbert Fuchs, Gerald Grant, David B. Dunson, Simon G. Gregory, and Allison E. Ashley-Koch. “Joint eQTL assessment of whole blood and dura mater tissue from individuals with Chiari type I malformation.” Bmc Genomics 16, no. 1 (January 22, 2015): 11. https://doi.org/10.1186/s12864-014-1211-8.Full Text Open Access Copy Link to Item
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Canale, A., and D. B. Dunson. “Bayesian multivariate mixed-scale density estimation.” Statistics and Its Interface 8, no. 2 (January 1, 2015): 195–201. https://doi.org/10.4310/SII.2015.v8.n2.a7.Full Text
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Chabout, Jonathan, Abhra Sarkar, David B. Dunson, and Erich D. Jarvis. “Male mice song syntax depends on social contexts and influences female preferences.” Front Behav Neurosci 9 (2015): 76. https://doi.org/10.3389/fnbeh.2015.00076.Full Text Open Access Copy Link to Item
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Kunihama, T., and D. B. Dunson. “Nonparametric Bayes inference on conditional independence.” Biometrika 103, no. 1 (January 1, 2015): 35–47. https://doi.org/10.1093/biomet/asv060.Full Text
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Zhou, Jing, Anirban Bhattacharya, Amy Herring, and David Dunson. “Bayesian factorizations of big sparse tensors.” Journal of the American Statistical Association 110, no. 512 (January 2015): 1562–76. https://doi.org/10.1080/01621459.2014.983233.Full Text
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Kessler, David C., Peter D. Hoff, and David B. Dunson. “Marginally specified priors for non-parametric Bayesian estimation.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 77, no. 1 (January 2015): 35–58. https://doi.org/10.1111/rssb.12059.Full Text
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Durante, D., and D. B. Dunson. “Nonparametric Bayes dynamic modelling of relational data.” Biometrika 101, no. 4 (December 1, 2014): 883–98. https://doi.org/10.1093/biomet/asu040.Full Text
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Yang, H., F. Liu, C. Ji, and D. Dunson. “Adaptive sampling for Bayesian geospatial models.” Statistics and Computing 24, no. 6 (November 1, 2014): 1101–10. https://doi.org/10.1007/s11222-013-9422-4.Full Text
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Gu, Kelvin, Debdeep Pati, and David B. Dunson. “Bayesian Multiscale Modeling of Closed Curves in Point Clouds.” Journal of the American Statistical Association 109, no. 508 (October 2014): 1481–94. https://doi.org/10.1080/01621459.2014.934825.Full Text
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Rodriguez, A., and D. B. Dunson. “Functional clustering in nested designs: Modeling variability in reproductive epidemiology studies.” Annals of Applied Statistics 8, no. 3 (September 1, 2014): 1416–42. https://doi.org/10.1214/14-AOAS751.Full Text
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Dunson, D. B. “Comment.” Journal of the American Statistical Association 109, no. 507 (July 3, 2014): 890–91. https://doi.org/10.1080/01621459.2014.955988.Full Text
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Wheeler, Matthew W., David B. Dunson, Sudha P. Pandalai, Brent A. Baker, and Amy H. Herring. “Mechanistic Hierarchical Gaussian Processes.” Journal of the American Statistical Association 109, no. 507 (July 2014): 894–904. https://doi.org/10.1080/01621459.2014.899234.Full Text
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Kessler, David C., Jack A. Taylor, and David B. Dunson. “Learning phenotype densities conditional on many interacting predictors.” Bioinformatics (Oxford, England) 30, no. 11 (June 2014): 1562–68. https://doi.org/10.1093/bioinformatics/btu040.Full Text
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Strawn, N., A. Armagan, R. Saab, L. Carin, and D. Dunson. “Finite sample posterior concentration in high-dimensional regression.” Information and Inference 3, no. 2 (June 1, 2014): 103–33. https://doi.org/10.1093/imaiai/iau003.Full Text
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Zhang, Jenny, Dereje Jima, Andrea B. Moffitt, Qingquan Liu, Magdalena Czader, Eric D. Hsi, Yuri Fedoriw, et al. “The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells.” Blood 123, no. 19 (May 8, 2014): 2988–96. https://doi.org/10.1182/blood-2013-07-517177.Full Text Link to Item
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Kundu, Suprateek, and David B. Dunson. “Bayes variable selection in semiparametric linear models.” Journal of the American Statistical Association 109, no. 505 (March 2014): 437–47. https://doi.org/10.1080/01621459.2014.881153.Full Text
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Cui, Kai, and David B. Dunson. “Generalized Dynamic Factor Models for Mixed-Measurement Time Series.” Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 23, no. 1 (February 2014): 169–91. https://doi.org/10.1080/10618600.2012.729986.Full Text
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Pati, Debdeep, and David B. Dunson. “Bayesian nonparametric regression with varying residual density.” Annals of the Institute of Statistical Mathematics 66, no. 1 (February 2014): 1–31. https://doi.org/10.1007/s10463-013-0415-z.Full Text
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Bhattacharya, A., D. Pati, and D. Dunson. “Anisotropic function estimation using multi-bandwidth Gaussian processes.” Annals of Statistics 42, no. 1 (January 1, 2014): 352–81. https://doi.org/10.1214/13-AOS1192.Full Text
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Chen, C. W. S., D. Dunson, S. Frühwirth-Schnatter, and S. G. Walker. “Special issue on Bayesian computing, methods and applications.” Computational Statistics and Data Analysis 71 (January 1, 2014): 273. https://doi.org/10.1016/j.csda.2013.10.011.Full Text
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Durante, D., B. Scarpa, and D. B. Dunson. “Locally adaptive factor processes for multivariate time series.” Journal of Machine Learning Research 15 (January 1, 2014): 1493–1522.
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Durante, D., and D. B. Dunson. “Bayesian dynamic financial networks with time-varying predictors.” Statistics and Probability Letters 93 (January 1, 2014): 19–26. https://doi.org/10.1016/j.spl.2014.06.015.Full Text
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Hannah, L. A., W. B. Powell, and D. B. Dunson. “Semiconvex regression for metamodeling-based optimization.” Siam Journal on Optimization 24, no. 2 (January 1, 2014): 573–97. https://doi.org/10.1137/130907070.Full Text
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Kundu, S., and D. B. Dunson. “Latent factor models for density estimation.” Biometrika 101, no. 3 (January 1, 2014): 641–54. https://doi.org/10.1093/biomet/asu019.Full Text
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Lin, L., and D. B. Dunson. “Bayesian monotone regression using Gaussian process projection.” Biometrika 101, no. 2 (January 1, 2014): 303–17. https://doi.org/10.1093/biomet/ast063.Full Text
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Pati, D., A. Bhattacharya, N. S. Pillai, and D. Dunson. “Posterior contraction in sparse bayesian factor models for massive covariance matrices.” Annals of Statistics 42, no. 3 (January 1, 2014): 1102–30. https://doi.org/10.1214/14-AOS1215.Full Text
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Scarpa, Bruno, and David B. Dunson. “Enriched Stick Breaking Processes for Functional Data.” Journal of the American Statistical Association 109, no. 506 (January 2014): 647–60. https://doi.org/10.1080/01621459.2013.866564.Full Text
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Wade, S., D. B. Dunson, S. Petrone, and L. Trippa. “Improving prediction from dirichlet process mixtures via enrichment.” Journal of Machine Learning Research 15 (January 1, 2014): 1041–71.
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Xing, Z., B. Nicholson, M. Jimenez, T. Veldman, L. Hudson, J. Lucas, D. Dunson, et al. “Bayesian modeling of temporal properties of infectious disease in a college student population.” Journal of Applied Statistics 41, no. 6 (January 1, 2014): 1358–82. https://doi.org/10.1080/02664763.2013.870138.Full Text
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Cornelis, B., Y. Yang, J. T. Vogelstein, A. Dooms, I. Daubechies, and D. Dunson. “Bayesian crack detection in ultra high resolution multimodal images of paintings.” 2013 18th International Conference on Digital Signal Processing, Dsp 2013, December 6, 2013. https://doi.org/10.1109/ICDSP.2013.6622710.Full Text Open Access Copy
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Armagan, A., D. B. Dunson, J. Lee, W. U. Bajwa, and N. Strawn. “Posterior consistency in linear models under shrinkage priors.” Biometrika 100, no. 4 (December 1, 2013): 1011–18. https://doi.org/10.1093/biomet/ast028.Full Text
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Canale, A., and D. B. Dunson. “Nonparametric Bayes modelling of count processes.” Biometrika 100, no. 4 (December 1, 2013): 801–16. https://doi.org/10.1093/biomet/ast037.Full Text
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Hannah, L. A., and D. B. Dunson. “Multivariate convex regression with adaptive partitioning.” Journal of Machine Learning Research 14 (November 1, 2013): 3153–88.
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Hannah, Lauren A., and David B. Dunson. “Multivariate Convex Regression with Adaptive Partitioning.” Journal of Machine Learning Research 14 (November 1, 2013): 3261–94.Link to Item
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Li, Daniel, Matthew P. Longnecker, and David B. Dunson. “Lipid adjustment for chemical exposures: accounting for concomitant variables.” Epidemiology (Cambridge, Mass.) 24, no. 6 (November 2013): 921–28. https://doi.org/10.1097/ede.0b013e3182a671e4.Full Text
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Lock, Eric F., and David B. Dunson. “Bayesian consensus clustering.” Bioinformatics (Oxford, England) 29, no. 20 (October 2013): 2610–16. https://doi.org/10.1093/bioinformatics/btt425.Full Text
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Salazar, E., D. B. Dunson, and L. Carin. “Analysis of space-time relational data with application to legislative voting.” Computational Statistics and Data Analysis 68 (July 29, 2013): 141–54. https://doi.org/10.1016/j.csda.2013.06.018.Full Text
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Zhu, Bin, Allison E. Ashley-Koch, and David B. Dunson. “Generalized admixture mapping for complex traits.” G3 (Bethesda) 3, no. 7 (July 8, 2013): 1165–75. https://doi.org/10.1534/g3.113.006478.Full Text Open Access Copy Link to Item
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Murray, Jared S., David B. Dunson, Lawrence Carin, and Joseph E. Lucas. “Bayesian Gaussian Copula Factor Models for Mixed Data.” Journal of the American Statistical Association 108, no. 502 (June 2013): 656–65. https://doi.org/10.1080/01621459.2012.762328.Full Text Open Access Copy
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Page, G., A. Bhattacharya, and D. Dunson. “Classification via bayesian nonparametric learning of affine subspaces.” Journal of the American Statistical Association 108, no. 501 (May 31, 2013): 187–201. https://doi.org/10.1080/01621459.2013.763566.Full Text
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Pati, Debdeep, David B. Dunson, and Surya T. Tokdar. “Posterior consistency in conditional distribution estimation.” Journal of Multivariate Analysis 116 (April 2013): 456–72. https://doi.org/10.1016/j.jmva.2013.01.011.Full Text
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Wang, E., E. Salazar, D. Dunson, and L. Carin. “Spatio-temporal modeling of legislation and votes.” Bayesian Analysis 8, no. 1 (March 22, 2013): 233–68. https://doi.org/10.1214/13-BA810.Full Text
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Zhang, Jenny, Vladimir Grubor, Cassandra L. Love, Anjishnu Banerjee, Kristy L. Richards, Piotr A. Mieczkowski, Cherie Dunphy, et al. “Genetic heterogeneity of diffuse large B-cell lymphoma.” Proc Natl Acad Sci U S A 110, no. 4 (January 22, 2013): 1398–1403. https://doi.org/10.1073/pnas.1205299110.Full Text Link to Item
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Armagan, Artin, David B. Dunson, and Jaeyong Lee. “GENERALIZED DOUBLE PARETO SHRINKAGE.” Statistica Sinica 23, no. 1 (January 2013): 119–43.
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Carlson, David E., Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, and Lawrence Carin. “Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling.” Ieee Transactions on Biomedical Engineering 61 (2013): 41–54.Open Access Copy
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Chen, B., G. Polatkan, G. Sapiro, D. Blei, D. Dunson, and L. Carin. “Deep Learning with Hierarchical Convolutional Factor Analysis.” Ieee Transactions on Pattern Analysis and Machine Intelligence, January 2013.
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Durante, D., B. Scarpa, and D. B. Dunson. “Locally adaptive bayesian multivariate time series.” Advances in Neural Information Processing Systems, January 1, 2013.
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Kunihama, Tsuyoshi, and David B. Dunson. “Bayesian modeling of temporal dependence in large sparse contingency tables.” Journal of the American Statistical Association 108, no. 504 (January 2013): 1324–38. https://doi.org/10.1080/01621459.2013.823866.Full Text
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Petralia, F., J. Vogelstein, and D. B. Dunson. “Multiscale dictionary learning for estimating conditional distributions.” Advances in Neural Information Processing Systems, January 1, 2013.Open Access Copy
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Xing, Z., B. Nicholson, M. Jimenez, T. Veldman, L. Hudson, J. Lucas, D. Dunson, et al. “Bayesian modeling of temporal properties of infectious disease in a college student population.” Journal of Applied Statistics, 2013.
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Yu, K., C. W. S. Chen, C. Reed, and D. B. Dunson. “Bayesian variable selection in quantile regression.” Statistics and Its Interface 6, no. 2 (January 1, 2013): 261–74. https://doi.org/10.4310/sii.2013.v6.n2.a9.Full Text
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Banerjee, A., D. B. Dunson, and S. T. Tokdar. “Efficient Gaussian process regression for large datasets.” Biometrika 100, no. 1 (2013): 75–89. https://doi.org/10.1093/biomet/ass068.Full Text Open Access Copy
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Zhu, Bin, and David B. Dunson. “Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes.” Journal of the American Statistical Association 108, no. 504 (January 2013). https://doi.org/10.1080/01621459.2013.838568.Full Text
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Zhu, Bin, David B. Dunson, and Allison E. Ashley-Koch. “Adverse subpopulation regression for multivariate outcomes with high-dimensional predictors.” Stat Med 31, no. 29 (December 20, 2012): 4102–13. https://doi.org/10.1002/sim.5520.Full Text Link to Item
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Ding, Mingtao, Lihan He, David Dunson, and Lawrence Carin. “Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.” Bayesian Analysis 7, no. 4 (December 2012): 813–40. https://doi.org/10.1214/12-ba727.Full Text
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Fox, E. B., and D. B. Dunson. “Multiresolution Gaussian processes.” Advances in Neural Information Processing Systems 1 (December 1, 2012): 737–45.
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Montagna, Silvia, Surya T. Tokdar, Brian Neelon, and David B. Dunson. “Bayesian latent factor regression for functional and longitudinal data.” Biometrics 68, no. 4 (December 2012): 1064–73. https://doi.org/10.1111/j.1541-0420.2012.01788.x.Full Text
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Petralia, F., V. Rao, and D. B. Dunson. “Repulsive mixtures.” Advances in Neural Information Processing Systems 3 (December 1, 2012): 1889–97.
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Love, Cassandra, Zhen Sun, Dereje Jima, Guojie Li, Jenny Zhang, Rodney Miles, Kristy L. Richards, et al. “The genetic landscape of mutations in Burkitt lymphoma.” Nat Genet 44, no. 12 (December 2012): 1321–25. https://doi.org/10.1038/ng.2468.Full Text Link to Item
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Hannah, L. A., and D. B. Dunson. “Ensemble methods for convex regression with applications to geometric programming based circuit design.” Proceedings of the 29th International Conference on Machine Learning, Icml 2012 1 (October 10, 2012): 369–76.
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Shterev, I. D., and D. B. Dunson. “Bayesian watermark attacks.” Proceedings of the 29th International Conference on Machine Learning, Icml 2012 1 (October 10, 2012): 695–702.
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Zhou, M., L. Li, D. Dunson, and L. Carin. “Lognormal and gamma mixed negative binomial regression.” Proceedings of the 29th International Conference on Machine Learning, Icml 2012 2 (October 10, 2012): 1343–50.Open Access Copy
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Bhattacharya, A., and D. Dunson. “Nonparametric Bayes classification and hypothesis testing on manifolds.” Journal of Multivariate Analysis 111 (October 1, 2012): 1–19. https://doi.org/10.1016/j.jmva.2012.02.020.Full Text
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Hua, Zhaowei, David B. Dunson, John H. Gilmore, Martin A. Styner, and Hongtu Zhu. “Semiparametric Bayesian local functional models for diffusion tensor tract statistics.” Neuroimage 63, no. 1 (October 2012): 460–74. https://doi.org/10.1016/j.neuroimage.2012.06.027.Full Text
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Bhattacharya, Abhishek, and David B. Dunson. “Strong consistency of nonparametric Bayes density estimation on compact metric spaces with applications to specific manifolds.” Annals of the Institute of Statistical Mathematics 64, no. 4 (August 2012): 687–714. https://doi.org/10.1007/s10463-011-0341-x.Full Text
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Bhattacharya, Anirban, and David B. Dunson. “Simplex Factor Models for Multivariate Unordered Categorical Data.” Journal of the American Statistical Association 107, no. 497 (March 2012): 362–77. https://doi.org/10.1080/01621459.2011.646934.Full Text
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Carin, Lawrence, Alfred Hero, Joseph Lucas, David Dunson, Minhua Chen, Ricardo Heñao, Arnau Tibau-Puig, Aimee Zaas, Christopher W. Woods, and Geoffrey S. Ginsburg. “High-Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections.” Ieee Signal Process Mag 29, no. 1 (January 1, 2012): 108–23. https://doi.org/10.1109/MSP.2011.943009.Full Text Link to Item
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Dunson, David B., and Chuanhua Xing. “Nonparametric Bayes Modeling of Multivariate Categorical Data.” Journal of the American Statistical Association 104, no. 487 (January 2012): 1042–51. https://doi.org/10.1198/jasa.2009.tm08439.Full Text
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Zhou, M., L. A. Hannah, D. B. Dunson, and L. Carin. “Beta-negative binomial process and poisson factor analysis.” Journal of Machine Learning Research 22 (January 1, 2012): 1462–71.Open Access Copy
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Zhou, Mingyuan, Haojun Chen, John Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David Dunson, Guillermo Sapiro, and Lawrence Carin. “Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society 21, no. 1 (January 2012): 130–44. https://doi.org/10.1109/tip.2011.2160072.Full Text
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Canale, Antonio, and David B. Dunson. “Bayesian Kernel Mixtures for Counts.” Journal of the American Statistical Association 106, no. 496 (December 2011): 1528–39. https://doi.org/10.1198/jasa.2011.tm10552.Full Text
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Gordon, G. J., and D. Dunson. “Preface to the proceedings of AISTATS 2011.” Journal of Machine Learning Research 15 (December 1, 2011): 1–2.
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Ren, L., Y. Wang, D. Dunson, and L. Carin. “The kernel beta process.” Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, Nips 2011, December 1, 2011.
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Zhang, X. X., D. B. Dunson, and L. Carin. “Hierarchical topic modeling for analysis of time-evolving personal choices.” Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, Nips 2011, December 1, 2011.
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Zhou, M., L. Carin, H. Yang, D. Dunson, and G. Sapiro. “Dependent hierarchical beta process for image interpolation and denoising.” Journal of Machine Learning Research 15 (December 1, 2011): 883–91.
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Chen, B., G. Polatkan, G. Sapiro, D. B. Dunson, and L. Carin. “The hierarchical beta process for convolutional factor analysis and deep learning.” Proceedings of the 28th International Conference on Machine Learning, Icml 2011, October 7, 2011, 361–68.
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Hannah, L. A., and D. B. Dunson. “Approximate dynamic programming for storage problems.” Proceedings of the 28th International Conference on Machine Learning, Icml 2011, October 7, 2011, 337–44.
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Wang, Lianming, and David B. Dunson. “Bayesian isotonic density regression.” Biometrika 98, no. 3 (September 2011): 537–51. https://doi.org/10.1093/biomet/asr025.Full Text
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Wang, Lianming, and David B. Dunson. “Semiparametric bayes' proportional odds models for current status data with underreporting.” Biometrics 67, no. 3 (September 2011): 1111–18. https://doi.org/10.1111/j.1541-0420.2010.01532.x.Full Text
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Yang, Hongxia, Sean O’Brien, and David B. Dunson. “Nonparametric Bayes Stochastically Ordered Latent Class Models.” J Am Stat Assoc 106, no. 495 (September 1, 2011): 807–17. https://doi.org/10.1198/jasa.2011.ap10058.Full Text Link to Item
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Zhou, M., H. Yang, G. Sapiro, D. Dunson, and L. Carin. “Covariate-dependent dictionary learning and sparse coding.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, August 18, 2011, 5824–27. https://doi.org/10.1109/ICASSP.2011.5947685.Full Text
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Armagan, A., and D. Dunson. “Sparse variational analysis of linear mixed models for large data sets.” Statistics and Probability Letters 81, no. 8 (August 1, 2011): 1056–62. https://doi.org/10.1016/j.spl.2011.02.029.Full Text
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Xing, Chuanhua, and David B. Dunson. “Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.” Plos Computational Biology 7, no. 7 (July 2011): e1002110. https://doi.org/10.1371/journal.pcbi.1002110.Full Text Open Access Copy
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Bhattacharya, A., and D. B. Dunson. “Sparse Bayesian infinite factor models.” Biometrika 98, no. 2 (June 2011): 291–306. https://doi.org/10.1093/biomet/asr013.Full Text
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Liu, Fei, David Dunson, and Fei Zou. “High-dimensional variable selection in meta-analysis for censored data.” Biometrics 67, no. 2 (June 2011): 504–12. https://doi.org/10.1111/j.1541-0420.2010.01466.x.Full Text
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Crandell, J. L., and D. B. Dunson. “Posterior simulation across nonparametric models for functional clustering.” Sankhya B 73, no. 1 (May 1, 2011): 42–61. https://doi.org/10.1007/s13571-011-0014-z.Full Text
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Page, Garritt L., and David B. Dunson. “Bayesian Local Contamination Models for Multivariate Outliers.” Technometrics : A Journal of Statistics for the Physical, Chemical, and Engineering Sciences 53, no. 2 (May 2011). https://doi.org/10.1198/tech.2011.10041.Full Text
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Dzirasa, Kafui, DeAnna L. McGarity, Anirban Bhattacharya, Sunil Kumar, Joseph S. Takahashi, David Dunson, Colleen A. McClung, and Miguel A. L. Nicolelis. “Impaired limbic gamma oscillatory synchrony during anxiety-related behavior in a genetic mouse model of bipolar mania.” J Neurosci 31, no. 17 (April 27, 2011): 6449–56. https://doi.org/10.1523/JNEUROSCI.6144-10.2011.Full Text Link to Item
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Carin, Lawrence, Richard G. Baraniuk, Volkan Cevher, David Dunson, Michael I. Jordan, Guillermo Sapiro, and Michael B. Wakin. “Learning Low-Dimensional Signal Models: A Bayesian approach based on incomplete measurements.” Ieee Signal Processing Magazine 28, no. 2 (March 2011). https://doi.org/10.1109/msp.2010.939733.Full Text
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Chen, M., J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin. “Erratum: Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: Algorithm and performance bounds (IEEE Transactions Signal Processing (2011)) 58,12 (6140-6155)).” Ieee Transactions on Signal Processing 59, no. 3 (March 1, 2011): 1329. https://doi.org/10.1109/TSP.2011.2107810.Full Text
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Pati, D., B. J. Reich, and D. B. Dunson. “Bayesian geostatistical modelling with informative sampling locations.” Biometrika 98, no. 1 (March 2011): 35–48. https://doi.org/10.1093/biomet/asq067.Full Text
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Reich, Brian J., Montserrat Fuentes, and David B. Dunson. “Bayesian Spatial Quantile Regression.” Journal of the American Statistical Association 106, no. 493 (March 2011): 6–20. https://doi.org/10.1198/jasa.2010.ap09237.Full Text
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Rodríguez, Abel, and David B. Dunson. “Nonparametric Bayesian models through probit stick-breaking processes.” Bayesian Analysis 6, no. 1 (March 2011). https://doi.org/10.1214/11-ba605.Full Text
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Chung, Yeonseung, and David B. Dunson. “The local Dirichlet process.” Annals of the Institute of Statistical Mathematics 63, no. 1 (February 2011): 59–80. https://doi.org/10.1007/s10463-008-0218-9.Full Text
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Shi, Minghui, and David B. Dunson. “Bayesian Variable Selection via Particle Stochastic Search.” Statistics & Probability Letters 81, no. 2 (February 2011): 283–91. https://doi.org/10.1016/j.spl.2010.10.011.Full Text
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Armagan, A., D. B. Dunson, and M. A. Clyde. “Generalized Beta Mixtures of Gaussians.” Edited by J. Shawe-Taylor, R. S. Zemel, and P. L. Bartlett. Advances in Neural Information Processing Systems 24 (2011): 523–31.
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Chen, Haojun, David B. Dunson, and Lawrence Carin. “Topic Modeling with Nonparametric Markov Tree.” Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning 2011 (January 2011): 377–84.
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Chen, Minhua, Aimee Zaas, Christopher Woods, Geoffrey S. Ginsburg, Joseph Lucas, David Dunson, and Lawrence Carin. “Predicting Viral Infection From High-Dimensional Biomarker Trajectories.” J Am Stat Assoc 106, no. 496 (January 1, 2011): 1259–79. https://doi.org/10.1198/jasa.2011.ap10611.Full Text Link to Item
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Ren, Lu, Lan Du, Lawrence Carin, and David B. Dunson. “Logistic Stick-Breaking Process.” Journal of Machine Learning Research : Jmlr 12, no. Jan (January 2011): 203–39.
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Zhang, XianXing, David B. Dunson, and Lawrence Carin. “Tree-Structured Infinite Sparse Factor Model.” Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning 2011 (January 2011): 785–92.
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Zhou, M., C. Wang, M. Chen, J. Paisley, D. Dunson, and L. Carin. “Nonparametric bayesian matrix completion.” 2010 Ieee Sensor Array and Multichannel Signal Processing Workshop, Sam 2010, December 20, 2010, 213–16. https://doi.org/10.1109/SAM.2010.5606741.Full Text
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Bhattacharya, Abhishek, and David B. Dunson. “Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.” Biometrika 97, no. 4 (December 2010): 851–65. https://doi.org/10.1093/biomet/asq044.Full Text
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Chen, Minhua, Jorge Silva, John Paisley, Chunping Wang, David Dunson, and Lawrence Carin. “Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.” Ieee Transactions on Signal Processing : A Publication of the Ieee Signal Processing Society 58, no. 12 (December 2010): 6140–55. https://doi.org/10.1109/tsp.2010.2070796.Full Text
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Gordon, G. J., and D. Dunson. “Preface to the Proceedings of AISTATS 2011.” Journal of Machine Learning Research 9 (December 1, 2010): 1–2.
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Wang, E., D. Liu, J. Silva, D. Dunson, and L. Carin. “Joint analysis of time-evolving binary matrices and associated documents.” Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, Nips 2010, December 1, 2010.
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Chen, Bo, Minhua Chen, John Paisley, Aimee Zaas, Christopher Woods, Geoffrey S. Ginsburg, Alfred Hero, Joseph Lucas, David Dunson, and Lawrence Carin. “Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.” Bmc Bioinformatics 11 (November 9, 2010): 552. https://doi.org/10.1186/1471-2105-11-552.Full Text Open Access Copy Link to Item
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Blei, David, Lawrence Carin, and David Dunson. “Probabilistic Topic Models: A focus on graphical model design and applications to document and image analysis.” Ieee Signal Processing Magazine 27, no. 6 (November 2010): 55–65. https://doi.org/10.1109/msp.2010.938079.Full Text
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Dunson, David B. “MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES.” Statistica Sinica 20, no. 4 (October 2010): 1395–1422.
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Cai, Bo, David B. Dunson, and Joseph B. Stanford. “Dynamic model for multivariate markers of fecundability.” Biometrics 66, no. 3 (September 2010): 905–13. https://doi.org/10.1111/j.1541-0420.2009.01327.x.Full Text
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Yang, Mingan, David B. Dunson, and Donna Baird. “Semiparametric Bayes hierarchical models with mean and variance constraints.” Computational Statistics & Data Analysis 54, no. 9 (September 2010): 2172–86. https://doi.org/10.1016/j.csda.2010.03.025.Full Text
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Stanford, Joseph B., Rafael T. Mikolajczyk, and David B. Dunson. “Are Chinese people really more fertile?” Fertility and Sterility 94, no. 3 (August 2010): e58. https://doi.org/10.1016/j.fertnstert.2010.05.004.Full Text
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Bornkamp, Björn, Katja Ickstadt, and David Dunson. “Stochastically ordered multiple regression.” Biostatistics (Oxford, England) 11, no. 3 (July 2010): 419–31. https://doi.org/10.1093/biostatistics/kxq001.Full Text
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Park, J. H., and D. B. Dunson. “Bayesian generalized product partition model.” Statistica Sinica 20, no. 3 (July 1, 2010): 1203–26.Open Access Copy
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Maclehose, Richard F., and David B. Dunson. “Bayesian semiparametric multiple shrinkage.” Biometrics 66, no. 2 (June 2010): 455–62. https://doi.org/10.1111/j.1541-0420.2009.01275.x.Full Text
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Ren, L., D. Dunson, S. Lindroth, and L. Carin. “Dynamic nonparametric bayesian models for analysis of music.” Journal of the American Statistical Association 105, no. 490 (June 1, 2010): 458–72. https://doi.org/10.1198/jasa.2009.ap08497.Full Text Open Access Copy
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Rodríguez, Abel, David B. Dunson, and Alan E. Gelfand. “Latent Stick-Breaking Processes.” Journal of the American Statistical Association 105, no. 490 (April 2010): 647–59. https://doi.org/10.1198/jasa.2010.tm08241.Full Text Open Access Copy
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Wang, Chunping, Xuejun Liao, Lawrence Carin, and David B. Dunson. “Classification with Incomplete Data Using Dirichlet Process Priors.” Journal of Machine Learning Research : Jmlr 11 (March 2010): 3269–3311.
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Mitra, Robin, and David Dunson. “Two-level stochastic search variable selection in GLMs with missing predictors.” The International Journal of Biostatistics 6, no. 1 (January 2010): Article-33. https://doi.org/10.2202/1557-4679.1173.Full Text
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Chung, Yeonseung, and David B. Dunson. “Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.” Journal of the American Statistical Association 104, no. 488 (December 2009): 1646–60. https://doi.org/10.1198/jasa.2009.tm08302.Full Text Open Access Copy
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Dunson, D. B. “Comment on article by Craigmile et al.” Bayesian Analysis 4, no. 1 (December 1, 2009): 41–44. https://doi.org/10.1214/09-BA401B.Full Text
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Ren, L., D. B. Dunson, S. Lindroth, and L. Carin. “Music analysis with a Bayesian dynamic model.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, September 23, 2009, 1681–84. https://doi.org/10.1109/ICASSP.2009.4959925.Full Text
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Wang, C., Q. An, L. Carin, and D. B. Dunson. “Multi-task classification with infinite local experts.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, September 23, 2009, 1569–72. https://doi.org/10.1109/ICASSP.2009.4959897.Full Text
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Scarpa, Bruno, and David B. Dunson. “Bayesian hierarchical functional data analysis via contaminated informative priors.” Biometrics 65, no. 3 (September 2009): 772–80. https://doi.org/10.1111/j.1541-0420.2008.01163.x.Full Text
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Baird, Donna D., Greg Travlos, Ralph Wilson, David B. Dunson, Michael C. Hill, Aimee A. D’Aloisio, Stephanie J. London, and Joel M. Schectman. “Uterine leiomyomata in relation to insulin-like growth factor-I, insulin, and diabetes.” Epidemiology (Cambridge, Mass.) 20, no. 4 (July 2009): 604–10. https://doi.org/10.1097/ede.0b013e31819d8d3f.Full Text
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Ghosh, Joyee, and David B. Dunson. “Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis.” Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 18, no. 2 (June 2009): 306–20. https://doi.org/10.1198/jcgs.2009.07145.Full Text
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Dunson, David B. “Bayesian nonparametric hierarchical modeling.” Biometrical Journal. Biometrische Zeitschrift 51, no. 2 (April 2009): 273–84. https://doi.org/10.1002/bimj.200800183.Full Text
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MacLehose, R. F., and D. B. Dunson. “Nonparametric Bayes kernel-based priors for functional data analysis.” Statistica Sinica 19, no. 2 (April 1, 2009): 611–29.
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Ji, S., D. Dunson, and L. Carin. “Multitask compressive sensing.” Ieee Transactions on Signal Processing 57, no. 1 (January 29, 2009): 92–106. https://doi.org/10.1109/TSP.2008.2005866.Full Text
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Bigelow, Jamie L., and David B. Dunson. “Bayesian semiparametric joint models for functional predictors.” Journal of the American Statistical Association 104, no. 485 (January 2009): 26–36. https://doi.org/10.1198/jasa.2009.0001.Full Text
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Du, Lan, Lu Ren, David B. Dunson, and Lawrence Carin. “A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.” Advances in Neural Information Processing Systems 2009 (January 2009): 486–94.
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Dunson, David B. “Nonparametric Bayes local partition models for random effects.” Biometrika 96, no. 2 (January 2009): 249–62. https://doi.org/10.1093/biomet/asp021.Full Text
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Rodriguez, Abel, David B. Dunson, and Jack Taylor. “Bayesian hierarchically weighted finite mixture models for samples of distributions.” Biostatistics (Oxford, England) 10, no. 1 (January 2009): 155–71. https://doi.org/10.1093/biostatistics/kxn024.Full Text
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Rodríguez, Abel, David B. Dunson, and Alan E. Gelfand. “Bayesian Nonparametric Functional Data Analysis Through Density Estimation.” Biometrika 96, no. 1 (January 2009): 149–62. https://doi.org/10.1093/biomet/asn054.Full Text
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Wang, L., and D. B. Dunson. “Fast Bayesian inference in Dirichlet process mixture models (Accepted).” Journal of Computational & Graphical Statistics, 2009.
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Armagan, A., and D. B. Dunson. “Sparse variational analysis of large longitudinal data sets (Submitted).” Statistics & Probability Letters, 2009.
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Wang, L., and D. B. Dunson. “Semiparametric Bayes multiple testing: Applications to tumor data. (Accepted)” Biometrics, 2009.
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Dunson, David B., and Shyamal D. Peddada. “Bayesian nonparametric inference on stochastic ordering.” Biometrika 95, no. 4 (December 2008): 859–74. https://doi.org/10.1093/biomet/asn043.Full Text
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Rodríguez, A., D. B. Dunson, and A. E. Gelfand. “The nested Dirichlet process: Rejoinder.” Journal of the American Statistical Association 103, no. 483 (September 1, 2008): 1153–54. https://doi.org/10.1198/016214508000000616.Full Text
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Ni, K., J. Paisley, L. Carin, and D. Dunson. “Multi-task learning for analyzing and sorting large databases of sequential data.” Ieee Transactions on Signal Processing 56, no. 8 II (August 1, 2008): 3918–31. https://doi.org/10.1109/TSP.2008.924798.Full Text
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Elliott, Leslie, John Henderson, Kate Northstone, Grace Y. Chiu, David Dunson, and Stephanie J. London. “Prospective study of breast-feeding in relation to wheeze, atopy, and bronchial hyperresponsiveness in the Avon Longitudinal Study of Parents and Children (ALSPAC).” The Journal of Allergy and Clinical Immunology 122, no. 1 (July 2008): 49-54.e3. https://doi.org/10.1016/j.jaci.2008.04.001.Full Text
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Dunson, D. B., A. H. Herring, and S. M. Engel. “Bayesian selection and clustering of polymorphisms in functionally related genes.” Journal of the American Statistical Association 103, no. 482 (June 1, 2008): 534–46. https://doi.org/10.1198/016214507000000554.Full Text
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Dunson, D. B., and J. H. Park. “Kernel stick-breaking processes.” Biometrika 95, no. 2 (June 1, 2008): 307–23. https://doi.org/10.1093/biomet/asn012.Full Text
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Pennell, Michael L., and David B. Dunson. “Nonparametric bayes testing of changes in a response distribution with an ordinal predictor.” Biometrics 64, no. 2 (June 2008): 413–23. https://doi.org/10.1111/j.1541-0420.2007.00885.x.Full Text
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Dunson, D. B. “Comment.” Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 40–41. https://doi.org/10.1198/016214507000001436.Full Text
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Dunson, D. B., Y. Xue, and L. Carin. “The matrix stick-breaking process: Flexible Bayes meta-analysis.” Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 317–27. https://doi.org/10.1198/016214507000001364.Full Text
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An, Q., C. Wang, I. Shterev, E. Wang, L. Carin, and D. B. Dunson. “Hierarchical kernel stick-breaking process for multi-task image analysis.” Proceedings of the 25th International Conference on Machine Learning, January 1, 2008, 17–24. https://doi.org/10.1145/1390156.1390159.Full Text
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Dunson, David B., Amy Herring, and Anna Maria Siega-Riz. “Bayesian Inference on Changes in Response Densities over Predictor Clusters.” Journal of the American Statistical Association 103, no. 484 (January 2008): 1508–17. https://doi.org/10.1198/016214508000001039.Full Text
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Qi, Y., D. Liu, D. Dunson, and L. Carin. “Multi-task compressive sensing with dirichlet process priors.” Proceedings of the 25th International Conference on Machine Learning, January 1, 2008, 768–75. https://doi.org/10.1145/1390156.1390253.Full Text
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Ren, L., D. B. Dunson, and L. Carin. “The dynamic hierarchical Dirichlet process.” Proceedings of the 25th International Conference on Machine Learning, January 1, 2008, 824–31. https://doi.org/10.1145/1390156.1390260.Full Text
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Rodriguez, A., D. B. Dunson, and A. E. Gelfand. “The nested Dirichlet process (with discussion).” Journal of the American Statistical Association, 2008.
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Rodriguez, A., D. B. Dunson, and A. E. Gelfand. “Nonparametric functional data analysis through Bayesian density estimation.” Biometrika 96 (2008): 149–62.
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Rodríguez, A., D. B. Dunson, and A. E. Gelfand. “The nested dirichlet process.” Journal of the American Statistical Association 103, no. 483 (January 1, 2008): 1131–54. https://doi.org/10.1198/016214508000000553.Full Text
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Yang, M., and D. B. Dunson. “Bayesian semiparametric structural equation models with latent variables (Submitted).” Psychometrika, 2008.
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Cai, B., and D. B. Dunson. “Bayesian multivariate isotonic regression splines: Applications to carcinogenicity studies.” Journal of the American Statistical Association 102, no. 480 (December 1, 2007): 1158–71. https://doi.org/10.1198/016214506000000942.Full Text
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Palomo, J., D. B. Dunson, and K. Bollen. “Bayesian Structural Equation Modeling,” December 1, 2007, 163–88. https://doi.org/10.1016/B978-044452044-9/50011-2.Full Text
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Dunson, David B. “Bayesian methods for latent trait modelling of longitudinal data.” Statistical Methods in Medical Research 16, no. 5 (October 2007): 399–415. https://doi.org/10.1177/0962280206075309.Full Text
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Pennell, Michael L., and David B. Dunson. “Fitting semiparametric random effects models to large data sets.” Biostatistics (Oxford, England) 8, no. 4 (October 2007): 821–34. https://doi.org/10.1093/biostatistics/kxm008.Full Text
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Scarpa, Bruno, David B. Dunson, and Elena Giacchi. “Bayesian selection of optimal rules for timing intercourse to conceive by using calendar and mucus.” Fertility and Sterility 88, no. 4 (October 2007): 915–24. https://doi.org/10.1016/j.fertnstert.2006.12.017.Full Text
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Bigelow, Jamie L., and David B. Dunson. “Bayesian adaptive regression splines for hierarchical data.” Biometrics 63, no. 3 (September 2007): 724–32. https://doi.org/10.1111/j.1541-0420.2007.00761.x.Full Text
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Ni, K., L. Carin, and D. Dunson. “Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.” Acm International Conference Proceeding Series 227 (August 23, 2007): 689–96. https://doi.org/10.1145/1273496.1273583.Full Text
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Xue, Y., D. Dunson, and L. Carin. “The matrix stick-breaking process for flexible multi-task learning.” Acm International Conference Proceeding Series 227 (August 23, 2007): 1063–70. https://doi.org/10.1145/1273496.1273630.Full Text
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Stanford, Joseph B., and David B. Dunson. “Effects of sexual intercourse patterns in time to pregnancy studies.” American Journal of Epidemiology 165, no. 9 (May 2007): 1088–95. https://doi.org/10.1093/aje/kwk111.Full Text
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Dunson, D. B. “Empirical bayes density regression.” Statistica Sinica 17, no. 2 (April 1, 2007): 481–504.
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Dunson, D. B., N. Pillai, and J. H. Park. “Bayesian density regression.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 69, no. 2 (April 1, 2007): 163–83. https://doi.org/10.1111/j.1467-9868.2007.00582.x.Full Text
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Scarpa, Bruno, and David B. Dunson. “Bayesian methods for searching for optimal rules for timing intercourse to achieve pregnancy.” Statistics in Medicine 26, no. 9 (April 2007): 1920–36. https://doi.org/10.1002/sim.2846.Full Text
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MacLehose, Richard F., David B. Dunson, Amy H. Herring, and Jane A. Hoppin. “Bayesian methods for highly correlated exposure data.” Epidemiology (Cambridge, Mass.) 18, no. 2 (March 2007): 199–207. https://doi.org/10.1097/01.ede.0000256320.30737.c0.Full Text
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Baird, Donna Day, David B. Dunson, Michael C. Hill, Deborah Cousins, and Joel M. Schectman. “Association of physical activity with development of uterine leiomyoma.” American Journal of Epidemiology 165, no. 2 (January 2007): 157–63. https://doi.org/10.1093/aje/kwj363.Full Text
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Kinney, S., and D. B. Dunson. “Fixed and random effects selection in linear and logistic models.” Biometrics 63 (2007): 690–98.
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Pennell, Michael L., and David B. Dunson. “Bayesian semiparametric dynamic frailty models for multiple event time data.” Biometrics 62, no. 4 (December 2006): 1044–52. https://doi.org/10.1111/j.1541-0420.2006.00571.x.Full Text
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Scarpa, Bruno, and David B. Dunson. “Bayesian selection of predictors of conception probabilities across the menstrual cycle.” Paediatric and Perinatal Epidemiology 20 Suppl 1 (November 2006): 30–37. https://doi.org/10.1111/j.1365-3016.2006.00768.x.Full Text
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Stanford, Joseph B., and David B. Dunson. “Foreword. Expanding Methodologies for Capturing Day-Specific Probabilities of Conception.” Paediatric and Perinatal Epidemiology 20 Suppl 1 (November 2006): 1–2. https://doi.org/10.1111/j.1365-3016.2006.00764.x.Full Text
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Dunson, David B. “Bayesian dynamic modeling of latent trait distributions.” Biostatistics (Oxford, England) 7, no. 4 (October 2006): 551–68. https://doi.org/10.1093/biostatistics/kxj025.Full Text
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O’Brien, S. M., L. L. Kupper, and D. B. Dunson. “Performance of tests of association in misspecified generalized linear models.” Journal of Statistical Planning and Inference 136, no. 9 (September 1, 2006): 3090–3100. https://doi.org/10.1016/j.jspi.2004.12.004.Full Text
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Cai, Bo, and David B. Dunson. “Bayesian covariance selection in generalized linear mixed models.” Biometrics 62, no. 2 (June 2006): 446–57. https://doi.org/10.1111/j.1541-0420.2005.00499.x.Full Text
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Chen, Z., and D. B. Dunson. “The authors replied as follows [2].” Biometrics 62, no. 2 (June 1, 2006): 623–24. https://doi.org/10.1111/j.1541-0420.2006.00586_2.x.Full Text
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Dunson, D. B. “Special issue of statistical methods in medical research on reproductive studies.” Statistical Methods in Medical Research 15, no. 2 (April 1, 2006): 91–92. https://doi.org/10.1191/0962280206sm432ed.Full Text
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Scarpa, Bruno, David B. Dunson, and Bernardo Colombo. “Cervical mucus secretions on the day of intercourse: an accurate marker of highly fertile days.” European Journal of Obstetrics, Gynecology, and Reproductive Biology 125, no. 1 (March 2006): 72–78. https://doi.org/10.1016/j.ejogrb.2005.07.024.Full Text
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Baird, Donna D., James S. Kesner, and David B. Dunson. “Luteinizing hormone in premenopausal women may stimulate uterine leiomyomata development.” Journal of the Society for Gynecologic Investigation 13, no. 2 (February 2006): 130–35. https://doi.org/10.1016/j.jsgi.2005.12.001.Full Text
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Dunson, D. B. “Bayesian Biostatistics.” Handbook of Statistics 25 (December 1, 2005): 743–61. https://doi.org/10.1016/S0169-7161(05)25025-3.Full Text
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Hans, Chris, and David B. Dunson. “Bayesian inferences on umbrella orderings.” Biometrics 61, no. 4 (December 2005): 1018–26. https://doi.org/10.1111/j.1541-0420.2005.00373.x.Full Text
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Gueorguieva, Ralitza V. “Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes.” Biometrics 61, no. 3 (September 2005): 862–66. https://doi.org/10.1111/j.1541-020x.2005.00409_1.x.Full Text
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Law, Dionne C Gesink, Mark A. Klebanoff, John W. Brock, David B. Dunson, and Matthew P. Longnecker. “Maternal serum levels of polychlorinated biphenyls and 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) and time to pregnancy.” American Journal of Epidemiology 162, no. 6 (September 2005): 523–32. https://doi.org/10.1093/aje/kwi240.Full Text
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Peddada, S. D., D. B. Dunson, and X. Tan. “Estimation of order-restricted means from correlated data.” Biometrika 92, no. 3 (September 1, 2005): 703–15. https://doi.org/10.1093/biomet/92.3.703.Full Text
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Gunn, Laura H., and David B. Dunson. “A transformation approach for incorporating monotone or unimodal constraints.” Biostatistics (Oxford, England) 6, no. 3 (July 2005): 434–49. https://doi.org/10.1093/biostatistics/kxi020.Full Text
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Dunson, D. B. “Bayesian semiparametric isotonic regression for count data.” Journal of the American Statistical Association 100, no. 470 (June 1, 2005): 618–27. https://doi.org/10.1198/016214504000001457.Full Text
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Dunson, David B., and Amy H. Herring. “Bayesian model selection and averaging in additive and proportional hazards models.” Lifetime Data Analysis 11, no. 2 (June 2005): 213–32. https://doi.org/10.1007/s10985-004-0384-x.Full Text
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Dunson, D. B., and J. A. Taylor. “Approximate Bayesian inference for quantites.” Journal of Nonparametric Statistics 17, no. 3 (April 1, 2005): 385–400. https://doi.org/10.1080/10485250500039049.Full Text
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Dunson, David B., Jamie L. Bigelow, and Bernardo Colombo. “Reduced fertilization rates in older men when cervical mucus is suboptimal.” Obstetrics and Gynecology 105, no. 4 (April 2005): 788–93. https://doi.org/10.1097/01.aog.0000154155.20366.ee.Full Text
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Dunson, David B., and Joseph B. Stanford. “Bayesian inferences on predictors of conception probabilities.” Biometrics 61, no. 1 (March 2005): 126–33. https://doi.org/10.1111/j.0006-341x.2005.031231.x.Full Text
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Longnecker, Matthew P., Mark A. Klebanoff, David B. Dunson, Xuguang Guo, Zhen Chen, Haibo Zhou, and John W. Brock. “Maternal serum level of the DDT metabolite DDE in relation to fetal loss in previous pregnancies.” Environmental Research 97, no. 2 (February 2005): 127–33. https://doi.org/10.1016/s0013-9351(03)00108-7.Full Text
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Dunson, David B., and Amy H. Herring. “Bayesian latent variable models for mixed discrete outcomes.” Biostatistics (Oxford, England) 6, no. 1 (January 2005): 11–25. https://doi.org/10.1093/biostatistics/kxh025.Full Text
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Herring, Amy H., David B. Dunson, and Nancy Dole. “Modeling the effects of a bidirectional latent predictor from multivariate questionnaire data.” Biometrics 60, no. 4 (December 2004): 926–35. https://doi.org/10.1111/j.0006-341x.2004.00248.x.Full Text
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Dunson, D. B., C. Holloman, C. Calder, and L. H. Gunn. “Bayesian modeling of multiple lesion onset and growth from interval-censored data.” Biometrics 60, no. 3 (September 2004): 676–83. https://doi.org/10.1111/j.0006-341x.2004.00217.x.Full Text
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O’Brien, Sean M., and David B. Dunson. “Bayesian multivariate logistic regression.” Biometrics 60, no. 3 (September 2004): 739–46. https://doi.org/10.1111/j.0006-341X.2004.00224.x.Full Text Link to Item
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Slama, Rémy, Béatrice Ducot, Niels Keiding, and Jean Bouyer. “Studying human fertility and environmental exposures.” Environmental Health Perspectives 112, no. 11 (August 2004): A604. https://doi.org/10.1289/ehp.112-1247502.Full Text Open Access Copy
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Wilcox, A. J., Donna Day Baird, David B. Dunson, D Robert McConnaughey, James S. Kesner, and Clarice R. Weinberg. “On the frequency of intercourse around ovulation: evidence for biological influences.” Human Reproduction (Oxford, England) 19, no. 7 (July 2004): 1539–43. https://doi.org/10.1093/humrep/deh305.Full Text
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Chen, Zhen, and David B. Dunson. “Bayesian estimation of survival functions under stochastic precedence.” Lifetime Data Analysis 10, no. 2 (June 2004): 159–73. https://doi.org/10.1023/b:lida.0000030201.12943.13.Full Text
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Dunson, David B., and Zhen Chen. “Selecting factors predictive of heterogeneity in multivariate event time data.” Biometrics 60, no. 2 (June 2004): 352–58. https://doi.org/10.1111/j.0006-341x.2004.00179.x.Full Text
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Neelon, Brian, and David B. Dunson. “Bayesian isotonic regression and trend analysis.” Biometrics 60, no. 2 (June 2004): 398–406. https://doi.org/10.1111/j.0006-341x.2004.00184.x.Full Text
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Bigelow, Jamie L., David B. Dunson, Joseph B. Stanford, René Ecochard, Christian Gnoth, and Bernardo Colombo. “Mucus observations in the fertile window: a better predictor of conception than timing of intercourse.” Human Reproduction (Oxford, England) 19, no. 4 (April 2004): 889–92. https://doi.org/10.1093/humrep/deh173.Full Text
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Dunson, David B., Donna D. Baird, and Bernardo Colombo. “Increased infertility with age in men and women.” Obstetrics and Gynecology 103, no. 1 (January 2004): 51–56. https://doi.org/10.1097/01.aog.0000100153.24061.45.Full Text
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Tingen, Candace, Joseph B. Stanford, and David B. Dunson. “Methodologic and statistical approaches to studying human fertility and environmental exposure.” Environmental Health Perspectives 112, no. 1 (January 2004): 87–93. https://doi.org/10.1289/ehp.6263.Full Text Open Access Copy
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Trouba, K., A. Nyska, M. Styblo, D. Dunson, L. Lomnitski, S. Grossman, G. Moser, et al. “Effect of antioxidants on the papilloma response and liver glutathione modulation mediated by arsenic in tg.ac transgenic mice.” Arsenic Exposure and Health Effects V, December 18, 2003, 283–93. https://doi.org/10.1016/B978-044451441-7/50022-1.Full Text
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Chen, Zhen, and David B. Dunson. “Random effects selection in linear mixed models.” Biometrics 59, no. 4 (December 2003): 762–69. https://doi.org/10.1111/j.0006-341x.2003.00089.x.Full Text
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Dunson, David B., and Amy H. Herring. “Bayesian inferences in the Cox model for order-restricted hypotheses.” Biometrics 59, no. 4 (December 2003): 916–23. https://doi.org/10.1111/j.0006-341x.2003.00106.x.Full Text
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Dunson, D. B. “Dynamic Latent Trait Models for Multidimensional Longitudinal Data.” Journal of the American Statistical Association 98, no. 463 (September 1, 2003): 555–63. https://doi.org/10.1198/016214503000000387.Full Text
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Dunson, David B., Zhen Chen, and Jean Harry. “A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.” Biometrics 59, no. 3 (September 2003): 521–30. https://doi.org/10.1111/1541-0420.00062.Full Text
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Dunson, David B., M. Watson, and Jack A. Taylor. “Bayesian latent variable models for median regression on multiple outcomes.” Biometrics 59, no. 2 (June 2003): 296–304. https://doi.org/10.1111/1541-0420.00036.Full Text
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Dunson, David B., and Brian Neelon. “Bayesian inference on order-constrained parameters in generalized linear models.” Biometrics 59, no. 2 (June 2003): 286–95. https://doi.org/10.1111/1541-0420.00035.Full Text
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Stanford, Joseph B., Ken R. Smith, and David B. Dunson. “Vulvar mucus observations and the probability of pregnancy.” Obstetrics and Gynecology 101, no. 6 (June 2003): 1285–93. https://doi.org/10.1016/s0029-7844(03)00358-2.Full Text
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Dunson, D. B. “Incorporating heterogeneous intercourse records into time to pregnancy models.” Mathematical Population Studies 10, no. 2 (April 1, 2003): 127–43. https://doi.org/10.1080/08898480306714.Full Text
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Baird, Donna Day, and David B. Dunson. “Why is parity protective for uterine fibroids?” Epidemiology (Cambridge, Mass.) 14, no. 2 (March 2003): 247–50. https://doi.org/10.1097/01.ede.0000054360.61254.27.Full Text
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Dunson, D. B., and B. Colombo. “Bayesian modeling of markers of day-specific fertility.” Journal of the American Statistical Association 98, no. 461 (March 1, 2003): 28–37. https://doi.org/10.1198/016214503388619067.Full Text
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Dunson, David B., Patricia Chulada, and Samuel J. Arbes. “Bayesian modeling of time-varying and waning exposure effects.” Biometrics 59, no. 1 (March 2003): 83–91. https://doi.org/10.1111/1541-0420.00010.Full Text
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Chulada, Patricia C., Samuel J. Arbes, David Dunson, and Darryl C. Zeldin. “Breast-feeding and the prevalence of asthma and wheeze in children: analyses from the Third National Health and Nutrition Examination Survey, 1988-1994.” The Journal of Allergy and Clinical Immunology 111, no. 2 (February 2003): 328–36. https://doi.org/10.1067/mai.2003.127.Full Text
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Baird, Donna Day, David B. Dunson, Michael C. Hill, Deborah Cousins, and Joel M. Schectman. “High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence.” American Journal of Obstetrics and Gynecology 188, no. 1 (January 2003): 100–107. https://doi.org/10.1067/mob.2003.99.Full Text
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Dunson, B., and Donna D. Baird. “Bayesian modeling of incidence and progression of disease from cross-sectional data.” Biometrics 58, no. 4 (December 2002): 813–22. https://doi.org/10.1111/j.0006-341x.2002.00813.x.Full Text
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Zeise, L., D. Hattis, M. Andersen, A. J. Bailer, S. Bayard, C. Chen, H. Clewell, et al. “Improving risk Assessment: Research opportunities in dose response modeling to improve risk assessment.” Human and Ecological Risk Assessment 8, no. 6 (October 1, 2002): 1421–44. https://doi.org/10.1080/20028091057448.Full Text
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Mikolajczyk, Rafael. “TwoDay Algorithm in predicting fertile time.” Human Reproduction (Oxford, England) 17, no. 7 (July 2002): 1925. https://doi.org/10.1093/humrep/17.7.1925.Full Text
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Tiano, Howard F., Charles D. Loftin, Jackie Akunda, Christopher A. Lee, Judson Spalding, Alisha Sessoms, David B. Dunson, et al. “Deficiency of either cyclooxygenase (COX)-1 or COX-2 alters epidermal differentiation and reduces mouse skin tumorigenesis.” Cancer Research 62, no. 12 (June 2002): 3395–3401.
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Dunson, David B., Bernardo Colombo, and Donna D. Baird. “Changes with age in the level and duration of fertility in the menstrual cycle.” Human Reproduction (Oxford, England) 17, no. 5 (May 2002): 1399–1403. https://doi.org/10.1093/humrep/17.5.1399.Full Text
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Dunson, David B., and Gregg E. Dinse. “Bayesian models for multivariate current status data with informative censoring.” Biometrics 58, no. 1 (March 2002): 79–88. https://doi.org/10.1111/j.0006-341x.2002.00079.x.Full Text
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Dunson, David B., and Donna D. Baird. “A proportional hazards model for incidence and induced remission of disease.” Biometrics 58, no. 1 (March 2002): 71–78. https://doi.org/10.1111/j.0006-341x.2002.00071.x.Full Text
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Dollé, Martijn E. T., Wendy K. Snyder, David B. Dunson, and Jan Vijg. “Mutational fingerprints of aging.” Nucleic Acids Research 30, no. 2 (January 2002): 545–49. https://doi.org/10.1093/nar/30.2.545.Full Text
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Dunson, D. B. “Bayesian modeling of the level and duration of fertility in the menstrual cycle.” Biometrics 57, no. 4 (December 2001): 1067–73. https://doi.org/10.1111/j.0006-341x.2001.01067.x.Full Text
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Dunson, D. B., I. Sinai, and B. Colombo. “The relationship between cervical secretions and the daily probabilities of pregnancy: effectiveness of the TwoDay Algorithm.” Human Reproduction (Oxford, England) 16, no. 11 (November 2001): 2278–82. https://doi.org/10.1093/humrep/16.11.2278.Full Text
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Wilcox, A. J., D. D. Baird, D. Dunson, R. McChesney, and C. R. Weinberg. “Natural limits of pregnancy testing in relation to the expected menstrual period.” Jama 286, no. 14 (October 2001): 1759–61. https://doi.org/10.1001/jama.286.14.1759.Full Text
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Nyska, A., L. Lomnitski, J. Spalding, D. B. Dunson, T. L. Goldsworthy, V. Ben-Shaul, S. Grossman, M. Bergman, and G. Boorman. “Erratum: Topical and oral administration of the natural water-soluble antioxidant from spinach reduces the multiplicity of papillomas in the Tg.AC mouse model (Toxicology Letters (2001) 122 (33-44) PII: S0378427401003459).” Toxicology Letters 123, no. 2–3 (September 15, 2001): 237. https://doi.org/10.1016/S0378-4274(01)00417-9.Full Text
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Robbins, W. A., K. L. Witt, J. K. Haseman, D. B. Dunson, L. Troiani, M. S. Cohen, C. D. Hamilton, et al. “Antiretroviral therapy effects on genetic and morphologic end points in lymphocytes and sperm of men with human immunodeficiency virus infection.” J Infect Dis 184, no. 2 (July 15, 2001): 127–35. https://doi.org/10.1086/322002.Full Text Link to Item
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Dunson, D. B. “Commentary: practical advantages of Bayesian analysis of epidemiologic data.” American Journal of Epidemiology 153, no. 12 (June 2001): 1222–26. https://doi.org/10.1093/aje/153.12.1222.Full Text
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Dunson, D. B., and D. D. Baird. “A flexible parametric model for combining current status and age at first diagnosis data.” Biometrics 57, no. 2 (June 2001): 396–403. https://doi.org/10.1111/j.0006-341x.2001.00396.x.Full Text
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Nyska, A., L. Lomnitski, J. Spalding, D. B. Dunson, T. L. Goldsworthy, V. Ben-Shaul, S. Grossman, M. Bergman, and G. Boorman. “Topical and oral administration of the natural water-soluble antioxidant from spinach reduces the multiplicity of papillomas in the Tg.AC mouse model.” Toxicology Letters 122, no. 1 (May 2001): 33–44. https://doi.org/10.1016/s0378-4274(01)00345-9.Full Text
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Wilcox, A. J., D. B. Dunson, C. R. Weinberg, J. Trussell, and D. D. Baird. “Likelihood of conception with a single act of intercourse: providing benchmark rates for assessment of post-coital contraceptives.” Contraception 63, no. 4 (April 2001): 211–15. https://doi.org/10.1016/s0010-7824(01)00191-3.Full Text
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Dunson, D. B. “Modeling of changes in tumor burden.” Journal of Agricultural, Biological, and Environmental Statistics 6, no. 1 (March 1, 2001): 38–48. https://doi.org/10.1198/108571101300325238.Full Text
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Dunson, D. B., C. R. Weinberg, D. D. Baird, J. S. Kesner, and A. J. Wilcox. “Assessing human fertility using several markers of ovulation.” Statistics in Medicine 20, no. 6 (March 2001): 965–78. https://doi.org/10.1002/sim.716.Full Text
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Dunson, D. B., and S. D. Perreault. “Factor analytic models of clustered multivariate data with informative censoring.” Biometrics 57, no. 1 (March 2001): 302–8. https://doi.org/10.1111/j.0006-341x.2001.00302.x.Full Text
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Dunson, D. B., and G. E. Dinse. “Bayesian incidence analysis of animal tumorigenicity data.” Journal of the Royal Statistical Society. Series C: Applied Statistics 50, no. 2 (January 1, 2001): 125–41. https://doi.org/10.1111/1467-9876.00224.Full Text
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Dunson, D. B., and G. E. Dinse. “Distinguishing effects on tumor multiplicity and growth rate in chemoprevention experiments.” Biometrics 56, no. 4 (December 2000): 1068–75. https://doi.org/10.1111/j.0006-341x.2000.01068.x.Full Text
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Dunson, D. B., and H. Zhou. “A Bayesian Model for Fecundability and Sterility.” Journal of the American Statistical Association 95, no. 452 (December 1, 2000): 1054–62. https://doi.org/10.1080/01621459.2000.10474302.Full Text
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Wilcox, A. J., D. Dunson, and D. D. Baird. “The timing of the "fertile window" in the menstrual cycle: day specific estimates from a prospective study.” Bmj (Clinical Research Ed.) 321, no. 7271 (November 2000): 1259–62. https://doi.org/10.1136/bmj.321.7271.1259.Full Text
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Dunson, D. B., and K. R. Tindall. “Bayesian analysis of mutational spectra.” Genetics 156, no. 3 (November 2000): 1411–18. https://doi.org/10.1093/genetics/156.3.1411.Full Text
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Dunson, D. B. “Assessing overall risk in reproductive experiments.” Risk Analysis : An Official Publication of the Society for Risk Analysis 20, no. 4 (August 2000): 429–37. https://doi.org/10.1111/0272-4332.204042.Full Text
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Dunson, D. B., J. K. Haseman, A. P. van Birgelen, S. Stasiewicz, and R. W. Tennant. “Statistical analysis of skin tumor data from Tg.AC mouse bioassays.” Toxicological Sciences : An Official Journal of the Society of Toxicology 55, no. 2 (June 2000): 293–302. https://doi.org/10.1093/toxsci/55.2.293.Full Text
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Dunson, D. B., and C. R. Weinberg. “Modeling human fertility in the presence of measurement error.” Biometrics 56, no. 1 (March 2000): 288–92. https://doi.org/10.1111/j.0006-341x.2000.00288.x.Full Text
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Weinberg, C. R., and D. B. Dunson. “Some Issues in Assessing Human Fertility.” Journal of the American Statistical Association 95, no. 449 (March 1, 2000): 300–303. https://doi.org/10.1080/01621459.2000.10473928.Full Text
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Dunson, D. B. “Bayesian latent variable models for clustered mixed outcomes.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 62, no. 2 (January 1, 2000): 355–66. https://doi.org/10.1111/1467-9868.00236.Full Text
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Dunson, D. B. “Models for papilloma multiplicity and regression: Applications to transgenic mouse studies.” Journal of the Royal Statistical Society. Series C: Applied Statistics 49, no. 1 (January 1, 2000): 19–30. https://doi.org/10.1111/1467-9876.00176.Full Text
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Dunson, D. B., and C. R. Weinberg. “Accounting for unreported and missing intercourse in human fertility studies.” Statistics in Medicine 19, no. 5 (2000): 665–79. https://doi.org/10.1002/(SICI)1097-0258(20000315)19:5<665::AID-SIM391>3.0.CO;2-P.Full Text
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Dunson, D. B., and J. K. Haseman. “Modeling tumor onset and multiplicity using transition models with latent variables.” Biometrics 55, no. 3 (September 1999): 965–70. https://doi.org/10.1111/j.0006-341x.1999.00965.x.Full Text
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Dunson, W. A., and D. B. Dunson. “Factors influencing growth and survival of the killifish, Rivulus marmoratus, held inside enclosures in mangrove swamps.” Copeia, no. 3 (August 2, 1999): 661–68. https://doi.org/10.2307/1447598.Full Text
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Dunson, D. B., D. D. Baird, A. J. Wilcox, and C. R. Weinberg. “Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovulation.” Human Reproduction (Oxford, England) 14, no. 7 (July 1999): 1835–39. https://doi.org/10.1093/humrep/14.7.1835.Full Text
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Dunson, D. B., C. R. Weinberg, S. D. Perreault, and R. E. Chapin. “Summarizing the motion of self-propelled cells: applications to sperm motility.” Biometrics 55, no. 2 (June 1999): 537–43. https://doi.org/10.1111/j.0006-341x.1999.00537.x.Full Text
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Dunson, D. B. “Dose-dependent number of implants and implications in developmental toxicity.” Biometrics 54, no. 2 (June 1998): 558–69. https://doi.org/10.2307/3109763.Full Text
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Dunson, W. A., C. J. Paradise, and D. B. Dunson. “Inhibitory effect of low salinity on growth and reproduction of the estuarine sheepshead minnow, Cyprinodon variegatus.” Copeia, no. 1 (February 3, 1998): 235–39. https://doi.org/10.2307/1447727.Full Text
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Li, Didong, and David Dunson. “Efficient Manifold and Subspace Approximations with Spherelets.” Arxiv, n.d.Link to Item
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Li, Didong, and David Dunson. “Classification via local manifold approximation,” n.d.Link to Item
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Book Sections
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Dunson, D. B. “Nonparametric Bayes.” In Past, Present, and Future of Statistical Science, 281–91, 2014.
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Dunson, D. B., A. Bhattacharya, and J. E. Griffin. “Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels.” In Bayesian Statistics 9, Vol. 9780199694587, 2012. https://doi.org/10.1093/acprof:oso/9780199694587.003.0005.Full Text
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Weinberg, C. R., and D. B. Dunson. “Some issues in assessing human fertility.” In Statistics in the 21st Century, 42–49, 2001.
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Other Articles
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Wang, L., L. Zhengwu Zhang, and L. Dunson. “COMMON AND INDIVIDUAL STRUCTURE OF MULTIPLE NETWORKS,” n.d.Link to Item
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Conference Papers
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Tam, E., and D. Dunson. “Fiedler regularization: Learning neural networks with graph sparsity.” In 37th International Conference on Machine Learning, Icml 2020, PartF168147-12:9288–97, 2020.
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Thai, D. H., H. T. Wu, and D. B. Dunson. “Locally convex kernel mixtures: Bayesian subspace learning.” In Proceedings 18th Ieee International Conference on Machine Learning and Applications, Icmla 2019, 272–75, 2019. https://doi.org/10.1109/ICMLA.2019.00051.Full Text
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Boom, Willem van den, Rebecca A. Schroeder, Michael W. Manning, Tracy L. Setji, Gic-Owens Fiestan, and David B. Dunson. “Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries.” In Diabetes Care, 41:782–88, 2018. https://doi.org/10.2337/dc17-2232.Full Text Link to Item
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Han, S., X. Liao, D. B. Dunson, and L. Carin. “Variational Gaussian copula inference.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 829–38, 2016.
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Wang, X., D. Dunson, and C. Leng. “No penalty no tears: Least squares in high-dimensional linear models.” In 33rd International Conference on Machine Learning, Icml 2016, 4:2685–2706, 2016.
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Wang, X., D. Dunson, and C. Leng. “DECOrrelated feature space partitioning for distributed sparse regression.” In Advances in Neural Information Processing Systems, 802–10, 2016.
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Wang, Y., A. Canale, and D. Dunson. “Scalable geometric density estimation.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 857–65, 2016.
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Guo, F., and D. B. Dunson. “Uncovering systematic bias in ratings across categories: A Bayesian approach.” In Recsys 2015 Proceedings of the 9th Acm Conference on Recommender Systems, 317–20, 2015. https://doi.org/10.1145/2792838.2799683.Full Text
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Srivastava, S., V. Cevher, Q. Tran-Dinh, and D. B. Dunson. “WASP: Scalable Bayes via barycenters of subset posteriors.” In Journal of Machine Learning Research, 38:912–20, 2015.
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Van Den Boom, W., D. Dunson, and G. Reeves. “Quantifying uncertainty in variable selection with arbitrary matrices.” In 2015 Ieee 6th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2015, 385–88, 2015. https://doi.org/10.1109/CAMSAP.2015.7383817.Full Text
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Wang, X., C. Leng, and D. B. Dunson. “On the consistency theory of high dimensional variable screening.” In Advances in Neural Information Processing Systems, 2015-January:2431–39, 2015.
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Wang, X., F. Guo, K. A. Heller, and D. B. Dunson. “Parallelizing MCMC with random partition trees.” In Advances in Neural Information Processing Systems, 2015-January:451–59, 2015.
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Wang, Y., and D. Dunson. “Probabilistic curve learning: Coulomb repulsion and the electrostatic Gaussian process.” In Advances in Neural Information Processing Systems, 2015-January:1738–46, 2015.
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Yin, R., D. Dunson, B. Cornelis, B. Brown, N. Ocon, and I. Daubechies. “Digital cradle removal in X-ray images of art paintings.” In 2014 Ieee International Conference on Image Processing, Icip 2014, 4299–4303, 2014. https://doi.org/10.1109/ICIP.2014.7025873.Full Text
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Durante, D., and D. B. Dunson. “Bayesian logistic Gaussian process models for dynamic networks.” In Journal of Machine Learning Research, 33:194–201, 2014.
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Minsker, S., S. Srivastava, L. Lin, and D. B. Dunson. “Scalable and robust Bayesian inference via the median posterior.” In 31st International Conference on Machine Learning, Icml 2014, 5:3629–39, 2014.
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Rai, P., Y. Wang, S. Guo, G. Chen, D. Dunson, and L. Carin. “Scalable bayesian low-rank decomposition of incomplete multiway tensors.” In 31st International Conference on Machine Learning, Icml 2014, 5:3810–20, 2014.
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Wang, X., P. Peng, and D. B. Dunson. “Median selection subset aggregation for parallel inference.” In Advances in Neural Information Processing Systems, 3:2195–2203, 2014.
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Banerjee, A., J. Murray, and D. B. Dunson. “Bayesian learning of joint distributions of objects.” In Journal of Machine Learning Research, 31:1–9, 2013.
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Johndrow, J. E., K. Lum, and D. B. Dunson. “Diagonal orthant multinomial probit models.” In Journal of Machine Learning Research, 31:29–38, 2013.
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Fyshe, A., E. Fox, D. Dunson, and T. Mitchell. “Hierarchical latent dictionaries for models of brain activation.” In Journal of Machine Learning Research, 22:409–21, 2012.
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- Teaching & Mentoring
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Recent Courses
- STA 790-1: Special Topics in Statistics 2023
- STA 610L: Multilevel and Hierarchical Models 2022
- STA 701S: Readings in Statistical Science 2022
- STA 723: Case Studies in Bayesian Statistics 2022
- STA 941: Bayesian Nonparametric Models and Methods 2022
- STA 993: Independent Study 2022
- STA 995: Internship 2022
- STA 723: Case Studies in Bayesian Statistics 2021
- STA 790-1: Special Topics in Statistics 2021
- STA 841: Models and Methods for Categorical Data 2021
- Scholarly, Clinical, & Service Activities
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Presentations & Appearances
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