Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Emory University 1997
  • B.S., Pennsylvania State University 1994
• 

  Duke Appointment History

  • Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2013 - 2018
• Recognition
• 

  In the News

  • OCT 11, 2019

    Statistician Receives European Research Council Grant to Aid in Mapping Biodiversity Around the Globe

  • FEB 20, 2017

    Creative People Have Better-Connected Brains

  • FEB 15, 2017

    Dunson Awarded Carnegie Centenary Professorship

  • JUN 1, 2015

    Duke Research Blog

    Two Duke Teams Attempting to Map LinkedIn Universe

  • APR 1, 2015

    Mice Sing Like Songbirds to Woo Mates

  • APR 1, 2015

    The Washington Post

    Mice sing just like birds, but we can’t hear them

  • APR 1, 2015

    The Guardian

    Squeaky serenade: male mice woo females with song, scientists discover

  • JUN 28, 2013

    The Atlantic

    How long can you wait to have a baby?

  • JUN 4, 2013

    Exploring a New World of Social Data
• 

  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
• 

  Subject Headings

  • Algorithms
  • Artificial Intelligence
  • Bayes Theorem
  • Data Interpretation, Statistical
  • Epidemiologic Methods
  • Models, Statistical
  • Models, Theoretical
  • Monte Carlo Method
  • Multivariate Analysis
  • Probability
• Research
• 

  Selected Grants

  • A Planetary Inventory of Life - a New Synthesis Built on Big Data Combined with Novel Statistical Methods awarded by European Research Council 2020 - 2026
  • Duke University Program in Environmental Health awarded by National Institutes of Health 2013 - 2024
  • HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by  National Science Foundation 2019 - 2022
  • Reproducibility and Robustness of Dimensionality Reduction awarded by National Institutes of Health 2017 - 2022
  • An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency 2018 - 2022
  • Structured nonparametric methods for mixtures of exposures awarded by National Institutes of Health 2018 - 2022
  • Probabilistic learning of structure in complex data awarded by Office of Naval Research 2017 - 2021
  • CRCNS: Geometry-based Brain Connectome Analysis awarded by National Institutes of Health 2018 - 2021
  • Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health 2017 - 2021
  • Scalable probabilistic inference for huge multi-domain graphs awarded by Alibaba Innovative Research 2017 - 2020
  • 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
• Publications & Artistic Works
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  Selected Publications

  • Books

    • Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Bayesian data analysis, third edition, 2013.

  • Academic Articles

    • 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.
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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.
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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.
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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.
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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.
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    • Dunson, D., and T. Papamarkou. “Discussion.” International Statistical Review, January 1, 2020. https://doi.org/10.1111/insr.12375.
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    • Ferrari, F., and D. B. Dunson. “Bayesian Factor Analysis for Inference on Interactions.” Journal of the American Statistical Association, January 1, 2020. https://doi.org/10.1080/01621459.2020.1745813.
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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.
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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.
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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.
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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 2019): 1999–2028. https://doi.org/10.3150/18-bej1043.
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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.
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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.
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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.
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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.
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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.
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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 (January 1, 2019): 621–53. https://doi.org/10.1093/imaiai/iay022.
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    • Li, M., and D. B. Dunson. “Comparing and Weighting Imperfect Models Using D-Probabilities.” Journal of the American Statistical Association, January 1, 2019. https://doi.org/10.1080/01621459.2019.1611140.
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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.
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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.
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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, January 1, 2019. https://doi.org/10.1080/01621459.2019.1677240.
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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 (January 1, 2019). https://doi.org/10.1002/ecm.1370.
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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.
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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.
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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.
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    • Zhao, S., B. E. Engelhardt, S. Mukherjee, and D. B. Dunson. “Fast Moment Estimation for Generalized Latent Dirichlet Models.” Journal of the American Statistical Association 113, no. 524 (October 2, 2018): 1528–40. https://doi.org/10.1080/01621459.2017.1341839.
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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).
    • 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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • Wheeler, M. W., D. B. Dunson, and A. H. Herring. “Bayesian Local Extremum Splines.” Biometrika 104, no. 4 (December 2017): 939–52. https://doi.org/10.1093/biomet/asx039.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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). https://doi.org/10.1098/rspb.2017.0768.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Datta, Jyotishka, and David B. Dunson. “Bayesian inference on quasi-sparse count data.” Biometrika 103, no. 4 (December 8, 2016): 971–83. https://doi.org/10.1093/biomet/asw053.
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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.
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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.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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).
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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 (January 22, 2015): 11. https://doi.org/10.1186/s12864-014-1211-8.
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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.
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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.
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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.
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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.
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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 (January 1, 2015). https://doi.org/10.1093/imaiai/iau008.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Carlson, David E., Joshua T. Vogelstein, Joshua T. Qisong Wu, Joshua T. Wenzhao Lian, Joshua T. 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 Bio Medical Engineering 61, no. 1 (January 2014): 41–54. https://doi.org/10.1109/tbme.2013.2275751.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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 (January 1, 2014): 1416–42. https://doi.org/10.1214/14-AOAS751.
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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.
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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 (January 1, 2014): 103–33. https://doi.org/10.1093/imaiai/iau003.
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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.
    • 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.
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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.
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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.
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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.
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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.
    • Hannah, Lauren A., and David B. Dunson. “Multivariate Convex Regression with Adaptive Partitioning.” Journal of Machine Learning Research 14 (November 1, 2013): 3261–94.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
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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.
    • Durante, D., B. Scarpa, and D. B. Dunson. “Locally adaptive bayesian multivariate time series.” Advances in Neural Information Processing Systems, January 1, 2013.
    • 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.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
    • 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.
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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.
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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.
    • 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.
    • 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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
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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.
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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.
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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.
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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.
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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 28, 2011): e1002110. https://doi.org/10.1371/journal.pcbi.1002110.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
    • 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.
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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.
    • Wang, Lianming, and David B. Dunson. “Fast Bayesian Inference in Dirichlet Process Mixture Models.” Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20, no. 1 (January 2011). https://doi.org/10.1198/jcgs.2010.07081.
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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.
    • 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.
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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.
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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.
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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.
    • 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.
    • Yang, M., and D. B. Dunson. “Bayesian semiparametric structural equation models with latent variables.” Psychometrika 75, no. 4 (December 1, 2010): 675–93. https://doi.org/10.1007/s11336-010-9174-4.
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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.
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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.
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    • Dunson, David B. “MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES.” Statistica Sinica 20, no. 4 (October 2010): 1395–1422.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Wang, Lianming, and David B. Dunson. “Semiparametric bayes multiple testing: applications to tumor data.” Biometrics 66, no. 2 (June 2010): 493–501. https://doi.org/10.1111/j.1541-0420.2009.01301.x.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
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    • Armagan, A., and D. B. Dunson. “Sparse variational analysis of large longitudinal data sets.” Statistics & Probability Letters, 2009.
    • 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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • Rodriguez, A., D. B. Dunson, and A. E. Gelfand. “Nonparametric functional data analysis through Bayesian density estimation.” Biometrika 96 (2008): 149–62.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Kinney, Satkartar K., and David B. Dunson. “Fixed and random effects selection in linear and logistic models.” Biometrics 63, no. 3 (September 2007): 690–98. https://doi.org/10.1111/j.1541-0420.2007.00771.x.
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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.
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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.
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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.
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    • Dunson, D. B. “Empirical bayes density regression.” Statistica Sinica 17, no. 2 (April 1, 2007): 481–504.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Dunson, D. B., and K. R. Tindall. “Bayesian analysis of mutational spectra.” Genetics 156, no. 3 (November 2000): 1411–18.
    • 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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    • Johndrow, James E., Jonathan C. Mattingly, Sayan Mukherjee, and David Dunson. “Optimal approximating Markov chains for Bayesian inference,” n.d.
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    • Li, Didong, and David Dunson. “Efficient Manifold and Subspace Approximations with Spherelets.” Arxiv, n.d.
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    • Li, Didong, and David Dunson. “Classification via local manifold approximation,” n.d.
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  • Book Sections

    • 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.
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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.

  • Other Articles

    • Wang, L., Z. Zhang, and D. Dunson. “Common and individual structure of brain networks.” Annals of Applied Statistics, January 1, 2019. https://doi.org/10.1214/18-AOAS1193.
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  • Conference Papers

    • 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.
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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.
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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.
    • 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.
    • 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.
    • 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.
    • 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.
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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.
    • 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.
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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.
    • 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.
    • 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.
    • 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.
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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.
    • 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.
    • 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.
    • 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.
    • 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.
    • Johndrow, J. E., K. Lum, and D. B. Dunson. “Diagonal orthant multinomial probit models.” In Journal of Machine Learning Research, 31:29–38, 2013.
    • 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.
• Teaching & Mentoring
• 

  Recent Courses

  • STA 493: Research Independent Study 2020
  • STA 601L: Bayesian Statistical Modeling and Data Analysis 2020
  • STA 641: Statistical Learning and Bayesian Nonparametrics 2020
  • STA 723: Case Studies in Bayesian Statistics 2020
  • MATH 493: Research Independent Study 2019
  • STA 841: Models and Methods for Categorical Data 2019
  • STA 993: Independent Study 2019
  • ECE 493: Projects in Electrical and Computer Engineering 2018
  • STA 340: Introduction to Statistical Decision Analysis 2018
  • STA 623: Statistical Decision Theory 2018
• Scholarly, Clinical, & Service Activities
• 

  Presentations & Appearances

  • NeurIPS Tutorial Scalable Bayes . NeurIPS conference . December 3, 2018  2018
  • Beyond tech: Machine learning in science & policy. Alan Turing Institute . 2018  2018
  • Machine learning in science: Debunking the hype. 2018  2018

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