Merlise Clyde
Professor of Statistical Science
Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive kernel non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences.
Office Hours
See individual course pages for office hours associated with courses.
Current Appointments & Affiliations
- Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2011
- Director of Graduate Studies in Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2021
Contact Information
- 223E Old Chem Bldg, Box 90251, Durham, NC 27708
- Duke Box 90251, Durham, NC 27708-0251
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clyde@duke.edu
(919) 681-8440
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GitHub Profile
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Personal site
- Background
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Education, Training, & Certifications
- Ph.D., University of Minnesota, Twin Cities 1993
- M.S., University of California - Riverside 1988
- M.S., University of Alberta (Canada) 1986
- B.S., Oregon State University 1985
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Previous Appointments & Affiliations
- Chair in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2013 - 2019
- Associate Professor in the Institute for Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 2000 - 2011
- Assistant Professor in the Institute of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 1993 - 2000
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Leadership & Clinical Positions at Duke
- Chair Department of Statistical Science 2013-2019
- Recognition
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In the News
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MAY 22, 2020 Trinity College of Arts and Sciences -
DEC 9, 2013 Durham Herald-Sun
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Awards & Honors
- Hocking Distinguished Lecture. Department of Statistics, Texas A&M University. 2018
- 11th Annual Distinguished Professor S. James Press Endowed Lecture . University of California, Riverside. 2017
- Seymour Geisser Distinguished Lecture. University of Minnesota. 2017
- Distinguished Service Award. North Carolina Chapter of the American Statistical Association. 2016
- Zellner Medal. International Society for Bayesian Analysis. 2016
- Fellow. International Society for Bayesian Analysis. 2014
- ASA Fellow. American Statistical Association. 2005
- Expertise
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Subject Headings
- Bayes Theorem
- Bayesian Statistical Decision Theory
- Brain
- Brain Mapping
- Case-Control Studies
- DNA Damage
- DNA Repair
- Deep Brain Stimulation
- Disease Susceptibility
- Epilepsy
- Genes, p53
- Genetic Association Studies
- Genetic Predisposition to Disease
- Interleukin-18
- Logistic Models
- Models, Statistical
- Nonparametric Statistics
- Polymorphism, Single Nucleotide
- Probability
- ROC Curve
- Regression Analysis
- Risk
- Tumor Markers, Biological
- Tumor Suppressor Protein p53
- Research
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Selected Grants
- Quantifying and Communicating Numerical Model Uncertainty awarded by University of North Carolina - Chapel Hill 2019 - 2021
- Statistical and Applied Mathematical Science Institute awarded by University of North Carolina - Chapel Hill 2017 - 2020
- Models for Consortium Level Analysis of GxE Interaction in Complex Disease awarded by National Institutes of Health 2012 - 2015
- Advances in Bayesian Model Choice awarded by National Science Foundation 2011 - 2015
- Bayesian Modeling and Optimal Design for Studies of Gene-Environment Association awarded by National Institutes of Health 2007 - 2011
- Multimodality Word-Finding in Neurosurgical Language Mapping awarded by National Institutes of Health 2007 - 2011
- Collaborative Research: Adaptive Experimental Design for Astronomical Exploration awarded by National Science Foundation 2005 - 2010
- Large Scale Model Averaging and Model Selection awarded by National Science Foundation 2004 - 2007
- SCREMS: Distributed Environments for Stochastic Computation awarded by National Science Foundation 2004 - 2007
- Spatial-temporal Models for Environmental Health Effects awarded by Environmental Protection Agency 2001 - 2006
- Model Uncertainty, Model Selection, and Robustness with Application in Environmental Science awarded by National Science Foundation 1998 - 2004
- Model Uncertainty in Health Effect Studies of Particulate Matter awarded by Environmental Protection Agency 1999 - 2001
- Model Uncertainty In Prediction, Variable Selection and Related Decision Problems awarded by National Science Foundation 1996 - 2000
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Bergh, D. V. D., Clyde, M. A., Gupta, A. R. K. N., de Jong, T., Gronau, Q. F., Marsman, M., … Wagenmakers, E.-J. (2021). A tutorial on Bayesian multi-model linear regression with BAS and JASP. Behavior Research Methods, 53(6), 2351–2371. https://doi.org/10.3758/s13428-021-01552-2Full Text Open Access Copy
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Li, Y., & Clyde, M. A. (2018). Mixtures of g-priors in Generalized Linear Models. Journal of the American Statistical Association, 113(524), 1828–1845. https://doi.org/10.1080/01621459.2018.1469992Full Text Open Access Copy Link to Item
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Day, D. B., Clyde, M. A., Xiang, J., Li, F., Cui, X., Mo, J., … Zhang, J. J. (2018). Age modification of ozone associations with cardiovascular disease risk in adults: a potential role for soluble P-selectin and blood pressure. Journal of Thoracic Disease, 10(7), 4643–4652. https://doi.org/10.21037/jtd.2018.06.135Full Text
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Day, D. B., Xiang, J., Mo, J., Clyde, M. A., Weschler, C. J., Li, F., … Zhang, J. (2018). Combined use of an electrostatic precipitator and a high-efficiency particulate air filter in building ventilation systems: Effects on cardiorespiratory health indicators in healthy adults. Indoor Air, 28(3), 360–372. https://doi.org/10.1111/ina.12447Full Text
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Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., … Johnson, V. E. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10. https://doi.org/10.1038/s41562-017-0189-zFull Text Open Access Copy
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RE: “RISK PREDICTION FOR EPITHELIAL OVARIAN CANCER IN 11 UNITED STATES–BASED CASE-CONTROL STUDIES: INCORPORATION OF EPIDEMIOLOGIC RISK FACTORS AND 17 CONFIRMED GENETIC LOCI”. (2017). American Journal of Epidemiology, 186(1), 130–130. https://doi.org/10.1093/aje/kwx151Full Text
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Clyde, M. A., Palmieri Weber, R., Iversen, E. S., Poole, E. M., Doherty, J. A., Goodman, M. T., … , on behalf of the Ovarian Cancer Association Consortium, . (2016). Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. Am J Epidemiol, 184(8), 579–589. https://doi.org/10.1093/aje/kww091Full Text Open Access Copy Link to Item
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Chou, N. D., Serafini, S., Grant, G. A., Clyde, M., Komisarow, J., & Muh, C. R. (2016). 127 Multimodality Word-Finding Distinctions in Pediatric Cortical Stimulation Mapping. Neurosurgery, 63 Suppl 1, 152. https://doi.org/10.1227/01.neu.0000489697.88290.88Full Text Link to Item
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Chou, N. D., Serafini, S., Grant, G. A., Clyde, M., Komisarow, J., & Muh, C. R. (2016). 127 Multimodality Word-Finding Distinctions in Pediatric Cortical Stimulation Mapping. Neurosurgery, 63(CN_suppl_1), 152. https://doi.org/10.1227/01.neu.0000489697.88290.88Full Text Link to Item
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Iversen, E. S., Lipton, G., Clyde, M. A., & Monteiro, A. N. A. (2014). Functional Annotation Signatures of Disease Susceptibility Loci Improve SNP Association Analysis. Bmc Genomics, 15, 398–398. https://doi.org/10.1186/1471-2164-15-398Full Text Open Access Copy
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Clyde, M. A., & Ghosh, J. (2012). Finite population estimators in stochastic search variable selection. Biometrika, 99(4), 981–988. https://doi.org/10.1093/biomet/ass040Full Text Open Access Copy
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Loredo, T. J., Berger, J. O., Chernoff, D. F., Clyde, M. A., & Liu, B. (2012). Bayesian methods for analysis and adaptive scheduling of exoplanet observations. Statistical Methodology, 9(1–2), 101–114. https://doi.org/10.1016/j.stamet.2011.07.005Full Text
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Ghosh, J., & Clyde, M. A. (2011). Rao-blackwellization for Bayesian variable selection and model averaging in linear and binary regression: A novel data augmentation approach. Journal of the American Statistical Association, 106(495), 1041–1052. https://doi.org/10.1198/jasa.2011.tm10518Full Text
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Clyde, M. A., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80–101. https://doi.org/10.1198/jcgs.2010.09049Full Text
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Armagan, A., Dunson, D. B., & Clyde, M. A. (2011). Generalized Beta Mixtures of Gaussians. Advances in Neural Information Processing Systems, 24, 523–531.
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House, L. L., Clyde, M. A., & Wolpert, R. L. (2011). Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy. The Annals of Applied Statistics, 5(2B), 1488–1511. https://doi.org/10.1214/10-AOAS450Full Text Open Access Copy Link to Item
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Wolpert, R. L., Clyde, M. A., & Tu, C. (2011). Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels. Annals of Statistics, 39(4), 1916–1962. https://doi.org/10.1214/11-AOS889Full Text Open Access Copy
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Wilson, M. A., Iversen, E. S., Clyde, M. A., Schmidler, S. C., & Schildkraut, J. M. (2010). BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES. Ann Appl Stat, 4(3), 1342–1364. https://doi.org/10.1214/09-aoas322Full Text Open Access Copy Link to Item
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Schildkraut, J. M., Iversen, E. S., Wilson, M. A., Clyde, M. A., Moorman, P. G., Palmieri, R. T., … Berchuck, A. (2010). Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer. Plos One, 5(4), e10061. https://doi.org/10.1371/journal.pone.0010061Full Text Open Access Copy Link to Item
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Jesneck, J. L., Mukherjee, S., Yurkovetsky, Z., Clyde, M., Marks, J. R., Lokshin, A. E., & Lo, J. Y. (2009). Do serum biomarkers really measure breast cancer? Bmc Cancer, 9, 164. https://doi.org/10.1186/1471-2407-9-164Full Text Link to Item
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Schildkraut, J., Iversen, E., Marks, J., Wilson, M., Clyde, M., Palmieri, R., … Berchuck, A. (2009). Association between serous invasive ovarian cancer and variants in candidate DNA damage response genes. Cancer Research, 69.Link to Item
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Schildkraut, J. M., Goode, E. L., Clyde, M. A., Iversen, E. S., Moorman, P. G., Berchuck, A., … Australian Ovarian Cancer Study Group, . (2009). Single nucleotide polymorphisms in the TP53 region and susceptibility to invasive epithelial ovarian cancer. Cancer Res, 69(6), 2349–2357. https://doi.org/10.1158/0008-5472.CAN-08-2902Full Text Link to Item
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Zhou, X. K., Clyde, M. A., Garrett, J., Lourdes, V., O’Connell, M., Parmigiani, G., … Wiles, T. (2009). Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves. Annals of Applied Statistics, 3(2), 710–730. https://doi.org/10.1214/08-AOAS217Full Text
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Chu, J. H., Clyde, M. A., & Liang, F. (2009). Bayesian function estimation using continuous wavelet dictionaries. Statistica Sinica, 19(4), 1419–1438.Link to Item
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Palmieri, R. T., Wilson, M. A., Iversen, E. S., Clyde, M. A., Calingaert, B., Moorman, P. G., … Australian Ovarian Cancer Study Group, . (2008). Polymorphism in the IL18 gene and epithelial ovarian cancer in non-Hispanic white women. Cancer Epidemiol Biomarkers Prev, 17(12), 3567–3572. https://doi.org/10.1158/1055-9965.EPI-08-0548Full Text Link to Item
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Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association, 103, 110–123. https://doi.org/10.1198/016214507000001337Full Text Link to Item
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Clyde, M. A., Berger, J. O., Bullard, F., Ford, E. B., Jefferys, W. H., Luo, R., … Loredo, T. (2007). Current challenges in Bayesian model choice. Statistical Challenges in Modern Astronomy Iv, 371, 224–240.Link to Item
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Kuncel, A. M., Cooper, S. E., Wolgamuth, B. R., Clyde, M. A., Snyder, S. A., Montgomery, E. B., … Grill, W. M. (2006). Clinical response to varying the stimulus parameters in deep brain stimulation for essential tremor. Movement Disorders : Official Journal of the Movement Disorder Society, 21(11), 1920–1928. https://doi.org/10.1002/mds.21087Full Text
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Clyde, M., House, L., Tu, C., & Wolpert, R. L. (2005). Bayesian Nonparametric Function Estimation Using Overcomplete Representations and Levy Random Field Priors. Oberwolfach Reports, 2(4), 2628–2633. https://doi.org/10.4171/OWR/2005/47Full Text Open Access Copy
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House, L., Clyde, M. A., & Huang, Y. C. (2005). Bayesian Identification of Differential Gene Expression Induced by Metals in Human Bronchial Epithelial Cells. Bayesisan Analysis, 1(1), 105–120. https://doi.org/10.1214/06-BA103Full Text
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Goodman, A. M., Clyde, M. A., Burdick, D. S., Idriss, S. F., & Wolf, P. D. (2004). Minimum energy single-shock internal atrial defibrillation in sheep. J Interv Card Electrophysiol, 10(2), 131–138. https://doi.org/10.1023/B:JICE.0000019266.09648.f6Full Text Link to Item
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Clyde, M., & George, E. I. (2004). Model uncertainty. Statistical Science, 19(1), 81–94. https://doi.org/10.1214/088342304000000035Full Text
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Lee, H. K. H., & Clyde, M. A. (2004). Lossless online Bayesian bagging. Journal of Machine Learning Research, 5, 143–151.Open Access Copy Link to Item
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Clyde, M. A., & George, E. I. (2003). Invited discussion of ``Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis'' by J.S. Morris, M. Vannucci, P.J. Brown, and R.J. Carroll. Journal of the American Statistical Association, 98(463), 584–585. https://doi.org/10.1198/016214503000000440Full Text
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Dominici, F., Sheppard, L., & Clyde, M. (2003). Health effects of air pollution: A statistical review. International Statistical Review, 71(2), 243–276. https://doi.org/10.1111/j.1751-5823.2003.tb00195.xFull Text
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Clyde, M., & Chaloner, K. (2002). Constrained design strategies for improving normal approximations in nonlinear regression problems. Journal of Statistical Planning and Inference, 104(1), 175–196. https://doi.org/10.1016/S0378-3758(01)00239-7Full Text
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Clyde, M., & Lee, H. K. (2001). Bagging and the Bayesian Bootstrap. Artificial Intelligence and Statistics, 8, 169–174.Open Access Copy Link to Item
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Clyde, M. (2000). Model uncertainty and health effect studies for particulate matter. Environmetrics, 11(6), 745–763. https://doi.org/10.1002/1099-095X(200011/12)11:6<745::AID-ENV431>3.0.CO;2-NFull Text
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Clyde, M., & George, E. I. (2000). Flexible empirical Bayes estimation for wavelets. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 62(4), 681–698. https://doi.org/10.1111/1467-9868.00257Full Text
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Dominici, F., Parmigiani, G., & Clyde, M. (2000). Conjugate analysis of multivariate normal data with incomplete observations. Canadian Journal of Statistics, 28(3), 533–550. https://doi.org/10.2307/3315963Full Text
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Lamon, E. C., & Clyde, M. A. (2000). Accounting for model uncertainty in prediction of chlorophyll a in Lake Okeechobee. Journal of Agricultural, Biological, and Environmental Statistics, 5(3), 297–322. https://doi.org/10.2307/1400456Full Text
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Clyde, M. (1999). Comment. Statistical Science, 14(4), 401–404.
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Clyde, M. A. (1999). Comment on ``Bayesian Model Averaging: A Tutorial'' by Hoeting, JA., Madigan,D., Raftery, AE., and Volinsky, CT. Statistical Science, 14, 401–404. https://doi.org/10.1214/ss/1009212519Full Text Open Access Copy
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MacEachern, S. N., Clyde, M., & Liu, J. S. (1999). Sequential importance sampling for nonparametric {B}ayes models: {T}he next generation. The Canadian Journal of Statistics, 27, 251–267.
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Clyde, M. A., & Parmigiani, G. (1998). Protein construct storage: Bayesian variable selection and prediction with mixtures. Journal of Biopharmaceutical Statistics, 8(3), 431–443. https://doi.org/10.1080/10543409808835251Full Text
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Clyde, M., Parmigiani, G., & Vidakovic, B. (1998). Multiple shrinkage and subset selection in wavelets. Biometrika, 85(2), 391–401. https://doi.org/10.1093/biomet/85.2.391Full Text
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Clyde, M. A. (1997). Strategies for Model Mixing in Generalized Linear Models. Artificial Intelligence and Statistics, 6, 103–114.
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Clyde, M. A. (1997). Strategies for Model Mixing in Generalized Linear Models. Artificial Intelligence and Statistics, 6, 103–114.
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Clyde, M., & Chaloner, K. (1996). The equivalence of constrained and weighted designs in multiple objective design problems. Journal of the American Statistical Association, 91(435), 1236–1244. https://doi.org/10.1080/01621459.1996.10476993Full Text
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Clyde, M. A., DeSimone, H., & Parmigiani, G. (1996). Prediction via orthogonalized model mixing. Journal of the American Statistical Association, 91, 1197–1208. https://doi.org/10.2307/2291738Full Text
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Furnier, G. R., Stine, M., Mohn, C. A., & Clyde, M. A. (1991). Geographic patterns of variation in allozymes and height growth in white spruce. Canadian Journal of Forest Research, 21(5), 707–712. https://doi.org/10.1139/x91-097Full Text
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Furnier, G. R., Knowles, P., Clyde, M. A., & Dancik, B. P. (1987). EFFECTS OF AVIAN SEED DISPERSAL ON THE GENETIC STRUCTURE OF WHITEBARK PINE POPULATIONS. Evolution; International Journal of Organic Evolution, 41(3), 607–612. https://doi.org/10.1111/j.1558-5646.1987.tb05831.xFull Text
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Clyde, M. A. (1987). Radial and longitudinal variation in stem diameter increment of lodgepole pine, white spruce, and black spruce: species and crown class differences. Canadian Journal of Forest Research, 1223–1227.
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Clyde, M. A. (1987). A new computerized system for tree ring measurement and analysis. Forestry Chronicle, 63, 23–27.
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Furnier, G. R., Knowles, P., Clyde, M. A., & Dancik, B. P. (1987). Effects of avian seed dispersal on the genetic structure of whitebark pine populations. Evolution, 41(3), 607–612. https://doi.org/10.1111/j.1558-5646.1987.tb05831.xFull Text
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Book Sections
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Front Matter of Volume 8. (2015). In International Encyclopedia of the Social & Behavioral Sciences (pp. iii–iii). Elsevier. https://doi.org/10.1016/b978-0-08-097086-8.99009-3Full Text
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Clyde, M. A. (2015). Experimental Design: Bayesian Designs. In J. D. Wright (Ed.), International Encyclopedia of Social and Behavioral Sciences (Vol. 8, pp. 521–526). Science Direct. https://doi.org/10.1016/B978-0-08-097086-8.99009-3Full Text Link to Item
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Clyde, M. A. (2015). Experimental Design: Bayesian Designs. In J. D. Wright (Ed.), International Encyclopedia of Social and Behavioral Sciences (Vol. 8, pp. 521–526). Science Direct. https://doi.org/10.1016/B978-0-08-097086-8.99009-3Full Text Link to Item
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Serafini, S., Clyde, M. A., Husain, A. M., & Haglund, M. M. (2015). Brain Mapping and Monitoring. In A. M. Husain (Ed.), Practical Epilepsy (pp. 192–199). Demos Medical Publishing, LLC.Link to Item
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Clydec, M., & Iversen, E. S. (2013). Bayesian model averaging in the M-open framework. In Bayesian Theory and Applications (pp. 483–498). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199695607.003.0024Full Text Open Access Copy
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Clyde, M. A., & Wolpert, R. L. (2011). Discussion of ``Polson and Scott: Shrink globally, act locally: Sparse Bayesian regularization and prediction''. In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian Statistics 9 (pp. 528–529). Oxford University Press.
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Clyde, M. A., & Wolpert, R. L. (2011). Discussion of ``Polson and Scott: Shrink globally, act locally: Sparse Bayesian regularization and prediction''. In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian Statistics 9 (pp. 528–529). Oxford University Press.
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Clyde, M. A., & Wolpert, R. L. (2007). Nonparametric Function Estimation using Overcomplete Dictionaries (with Discussion). In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian Statistics 8 (pp. 91–114). Oxford University Press.
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Clyde, M. A., & Wolpert, R. L. (2007). Nonparametric Function Estimation using Overcomplete Dictionaries (with Discussion). In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian Statistics 8 (pp. 91–114). Oxford University Press.
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Clyde, M., House, L., & Wolpert, R. L. (2006). Nonparametric Models for Proteomic Peak Identification and Quantification. In K. A. Do, P. Muller, & M. Vannucci (Eds.), Bayesian Inference for Gene Expression and Proteomics (pp. 293–308). Cambridge University Press. https://doi.org/10.1017/CBO9780511584589.016Full Text
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Clyde, M. A., House, L. L., & Wolpert, R. L. (2006). Nonparametric Models for Proteomic Peak Identification and Quantification. In BAYESIAN INFERENCE FOR GENE EXPRESSION AND PROTEOMICS (pp. 293–308).Link to Item
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Clyde, M. (2003). Model Averaging. In S. J. Press (Ed.), Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. John Wiley & Sons.
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Clyde, M. (2003). Invited discussion of "Bayesian and Frequentist Multiple Testing, by C. Genovese and L. Wasserman". In J. M. Bernardo, M. J. Bayarri, A. P. Dawid, J. O. Berger, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian Statistics 7 (pp. 157–160). Oxford University Press.
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Clyde, M. (2001). Invited discussion of ``The Practical Implementation of Bayesian Model Selection'' by H. Chipman, E.I. George, and R.E. McCulloch. In P. Lahiri (Ed.), Model selection (Vol. 38, pp. 117–124). https://doi.org/10.1214/lnms/1215540965Full Text Open Access Copy
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Clyde, M. A. (2001). Experimental Design: Bayesian Designs. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences (pp. 5075–5081). New York, NY: Science Direct. https://doi.org/10.1016/B0-08-043076-7/00421-6Full Text Link to Item
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Clyde, M. A. (1999). Bayesian Model Averaging and Model Search Strategies (with Discussion). In J. M. Bernardo, A. P. Dawid, J. O. Berger, & A. F. M. Smith (Eds.), Bayesian Statistics 6 (Vol. 6, pp. 157–185). Oxford University Press.Link to Item
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Clyde, M. A., & George, E. I. (1999). Empirical Bayes estimation in wavelet nonparametric regression. In P. Muller & B. Vidakovic (Eds.), Bayesian Inference in Wavelet-Based Models (Vol. 141, pp. 309–322). Springer. https://doi.org/10.1007/978-1-4612-0567-8_19Full Text Link to Item
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Paddock, S., Clyde, M. A., & West, M. (1998). Mixture models in the exploration of structure-activity relationships in drug design. In C. Gastonis, R. E. Kass, B. Carlin, A. Carriquiry, A. German, M. West, & I. Verdinelli (Eds.), Bayesian Statistics in Science and Engineering: Case Studies IV (pp. 339–353). Springer. https://doi.org/10.1007/978-1-4612-1502-8_9Full Text
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Clyde, M. A., Parmigiani, G., & Vidakovic, B. (1997). Using Markov chain Monte Carlo to account for model uncertainty, with applications to wavelets. In L. Billard & N. I. Fisher (Eds.), Computing science and statistics. (Vol. 28, pp. 209–218). Fairfax Station, VA: Interface Foundation of North America,.
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Clyde, M. A., Parmigiani, G., & Vidakovic, B. (1997). Using Markov chain Monte Carlo to account for model uncertainty, with applications to wavelets. In L. Billard & N. I. Fisher (Eds.), Computing science and statistics. (Vol. 28, pp. 209–218). Fairfax Station, VA: Interface Foundation of North America,.
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Clyde, M. A., & Parmigiani, G. (1996). Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing. In J. C. Lee, W. O. Johnson, & A. Zellner (Eds.), Modelling and Prediction: Honoring Seymour Geisser (pp. 206–227). Springer. https://doi.org/10.1007/978-1-4612-2414-3_13Full Text
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Clyde, M. A., Muller, P., & Parmigiani, G. (1996). Inference and design strategies for a hierarchical logistic regression model. In D. A. Berry & D. K. Stangl (Eds.), Bayesian Biostatistics (Vol. 151, pp. 297–320). New York, NY: Marcel Dekker.Link to Item
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Clyde, M. A., Muller, P., & Parmigiani, G. (1996). Inference and design strategies for a hierarchical logistic regression model. In D. A. Berry & D. K. Stangl (Eds.), Bayesian Biostatistics (Vol. 151, pp. 297–320). New York, NY: Marcel Dekker.Link to Item
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Clyde, M. A. (1995). Bayesian Designs for Approximate Normality. In C. P. Kitsos & W. G. Muller (Eds.), MODA 4 -- Advances in Model--Oriented Data Analysis (pp. 25–35). Physica-Verlag. https://doi.org/10.1007/978-3-662-12516-8Full Text Link to Item
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Clyde, M. A., Muller, P., & Parmigiani, G. (1995). Optimal Design for Heart Defibrillators. In C. Gastonia, J. S. Hodges, R. E. Kass, & N. D. Singpurwall (Eds.), Bayesian Statistics in Science and Engineering: Case Studies II (Vol. 105, pp. 278–292). Springer-Verlag. https://doi.org/10.1007/978-1-4612-2546-1_7Full Text Link to Item
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Clyde, M., DeSimone, H., & Parmigiani, G. (1995). Discussion of "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance by A.E. Raftery, D.M. Madigan and C.T. Volinsky". In J. O. Berger, J. M. Bernardo, A. P. Dawid, & A. F. M. Smith (Eds.), Bayesian Statistics 5 (Vol. 5, pp. 341–341). Oxford University Press.
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Clyde, M. A. (1991). Logistic regression for spatial pair-potential models. In Spatial statistics and imaging (Vol. 20, pp. 14–30). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215460490Full Text
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Clyde, M. A. (1991). Logistic regression for spatial pair-potential models. In Spatial statistics and imaging (Vol. 20, pp. 14–30). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215460490Full Text
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Conference Papers
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Ness, R., Clyde, M., Palmieri, R., & Schildkraut, J. (2017). Risk prediction for ovarian cancer: epidemiologic risk factors plus confirmed genetic loci. In Bjog an International Journal of Obstetrics and Gynaecology (Vol. 124, pp. 159–159). WILEY.Link to Item
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Clyde, M. A., Weber, R. P., Ness, R., & Schildkraut, J. (2016). RISK PREDICTION FOR OVARIAN CANCER: EPIDEMIOLOGIC RISK FACTORS PLUS CONFIRMED GENETIC LOCI. In International Journal of Gynecological Cancer (Vol. 26, pp. 28–28). LIPPINCOTT WILLIAMS & WILKINS.Link to Item
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Clyde, M. A., DeSimone, H., & Parmigiani, G. (1994). A Comparison of Algorithms for Sampling Models. In Proceedings of the 1994 Joint Statistical Meetings; Section on Bayesian Statistical Science (pp. 211–216).Link to Item
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Software
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Clyde, M. A. (2016). BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling. CRAN. https://doi.org/10.5281/zenodo.59497Full Text Link to Item
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- Teaching & Mentoring
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Recent Courses
- STA 701S: Readings in Statistical Science 2023
- STA 702L: Bayesian Statistical Modeling and Data Analysis 2023
- STA 790-1: Special Topics in Statistics 2023
- STA 995: Internship 2023
- STA 996: Spring or Fall Internship 2023
- STA 702L: Bayesian Statistical Modeling and Data Analysis 2022
- STA 995: Internship 2022
- STA 493: Research Independent Study 2021
- STA 601L: Bayesian Statistical Modeling and Data Analysis 2021
- Scholarly, Clinical, & Service Activities
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Presentations & Appearances
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Service to the Profession
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Service to Duke
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