Merlise Clyde | Scholars@Duke

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

Contact Information

223E Old Chem Bldg, Box 90251, Durham, NC 27708
Duke Box 90251, Durham, NC 27708-0251
clyde@duke.edu (919) 681-8440
GitHub Profile
Personal Site
Statistical Science Profile

Background
Education, Training, & Certifications
- Ph.D., University of Minnesota, Twin Cities 1993
- M.S., University of California at Riverside 1988
- M.S., University of Alberta (Canada) 1986
- B.S., Oregon State University 1985
Duke Appointment History
- 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
Leadership & Clinical Positions at Duke
- Chair Department of Statistical Science 2013-2019
Recognition
In the News
- DEC 9, 2013 Durham Herald-Sun
  
  Duke trustees approve six new graduate programs
Awards & Honors
Expertise
Subject Headings
Research
Selected Grants
- Quantifying and Communicating Numerical Model Uncertainty awarded by University of North Carolina - Chapel Hill 2019 - 2020
- 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
- (98-0024) 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
- (96-0326) Model Uncertainty In Prediction, Variable Selection and Related Decision Problems awarded by National Science Foundation 1996 - 2000
External Relationships
- Coursera
- JASP
- Project Euclid
Publications & Artistic Works
Selected Publications
- Academic Articles
  - 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/10.1080/01621459.2018.1469992
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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.2018.06.135
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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.12447
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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-z
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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/kwx151
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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/kww091
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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.0000489697.88290.88
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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.0000489697.88290.88
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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/10.1186/1471-2164-15-398
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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/ass040
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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.2011.07.005
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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.tm10518
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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.2010.09049
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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.
  - 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-AOAS450
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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-AOS889
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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-aoas322
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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.0010061
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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/10.1186/1471-2407-9-164
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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.
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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-08-2902
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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-AOAS217
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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.
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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-08-0548
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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(481), 410–423. https://doi.org/10.1198/016214507000001337
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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.
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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.21087
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  - Berger, J., Dette, H., Lugosi, G., & Munk, A. (2005). Statistische und Probabilistische Methoden der Modellwahl. Oberwolfach Reports, 2611–2704. https://doi.org/10.4171/owr/2005/47
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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-BA103
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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. Journal of Interventional Cardiac Electrophysiology : An International Journal of Arrhythmias and Pacing, 10(2), 131–138. https://doi.org/10.1023/b:jice.0000019266.09648.f6
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  - Clyde, M., & George, E. I. (2004). Model uncertainty. Statistical Science, 19(1), 81–94. https://doi.org/10.1214/088342304000000035
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  - Lee, H. K. H., & Clyde, M. A. (2004). Lossless online Bayesian bagging. Journal of Machine Learning Research, 5, 143–151.
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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.
  - 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/10.1198/016214503000000440
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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)00239-7
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  - Clyde, M., & Lee, H. K. (2001). Bagging and the Bayesian Bootstrap. Artificial Intelligence and Statistics, 8, 169–174.
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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-N
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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/10.1111/1467-9868.00257
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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/10.2307/3315963
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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/10.2307/1400456
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  - Clyde, M. (1999). Comment. Statistical Science, 14(4), 401–404.
  - 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/1009212519
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  - MacEachern, S. N., Clyde, M., & Liu, J. S. (1999). Sequential importance sampling for nonparametric Bayes models: The next generation. Canadian Journal of Statistics, 27(2), 251–267. https://doi.org/10.2307/3315637
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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/10.1080/10543409808835251
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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/85.2.391
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  - Clyde, M. A. (1997). Strategies for Model Mixing in Generalized Linear Models. Artificial Intelligence and Statistics, 6, 103–114.
  - Clyde, M. A. (1997). Strategies for Model Mixing in Generalized Linear Models. Artificial Intelligence and Statistics, 6, 103–114.
  - 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/10.1080/01621459.1996.10476993
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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/10.2307/2291738
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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/x91-097
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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.
  - Clyde, M. A. (1987). A new computerized system for tree ring measurement and analysis. Forestry Chronicle, 63, 23–27.
  - 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.
- Book Sections
  - Clyde, M. A. (2015). Experimental Design: Bayesian Designs. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 521–526). https://doi.org/10.1016/B978-0-08-097086-8.42121-5
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  - Clyde, M. A. (2015). Experimental Design: Bayesian Designs. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 521–526). https://doi.org/10.1016/B978-0-08-097086-8.42121-5
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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.
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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/9780199695607.003.0024
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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.
  - 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.
  - 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.
  - 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.
  - Clyde, M. A., House, L., & Wolpert, R. L. (2006). Nonparametric Models for Proteomic Peak Identification and Quantification. In K.-A. Do, P. Müller, & M. Vannucci (Eds.), Bayesian Inference for Gene Expression and Proteomics (pp. 293–308). Cambridge University Press.
  - Clyde, M. (2003). Model Averaging. In S. J. Press (Ed.), Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. John Wiley & Sons.
  - 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.
  - Clyde, M. (2001). Discussion of Hugh Chipman, Edward I. George and Robert E. McCulloch, "The Practical Implementation of Bayesian Model Selection". In Institute of Mathematical Statistics Lecture Notes - Monograph Series (pp. 117–124). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215540965
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  - Clyde, M. A. (2001). Experimental Design: Bayesian Designs. In International Encyclopedia of the Social & Behavioral Sciences (pp. 5075–5081). Elsevier. https://doi.org/10.1016/b0-08-043076-7/00421-6
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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.
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  - Clyde, M. A., & George, E. I. (1999). Empirical Bayes Estimation in Wavelet Nonparametric Regression. In Bayesian Inference in Wavelet-Based Models (pp. 309–322). Springer New York. https://doi.org/10.1007/978-1-4612-0567-8_19
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  - Paddock, S., West, M., Young, S. S., & Clyde, M. (1999). Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design. In Case Studies in Bayesian Statistics (pp. 339–353). Springer New York. https://doi.org/10.1007/978-1-4612-1502-8_9
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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,.
  - 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,.
  - 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.
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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.
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  - Clyde, M., & Parmigiani, G. (1996). Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing. In Modelling and Prediction Honoring Seymour Geisser (pp. 206–227). Springer New York. https://doi.org/10.1007/978-1-4612-2414-3_13
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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/10.1007/978-3-662-12516-8
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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.
  - Clyde, M., Müller, P., & Parmigiani, G. (1995). Optimal Design for Heart Defibrillators. In Lecture Notes in Statistics (pp. 278–292). Springer New York. https://doi.org/10.1007/978-1-4612-2546-1_7
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  - Clyde, M., & Strauss, D. (1991). Logistic regression for spatial pair-potential models. In Institute of Mathematical Statistics Lecture Notes - Monograph Series (pp. 14–30). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215460490
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  - Clyde, M., & Strauss, D. (1991). Logistic regression for spatial pair-potential models. In Institute of Mathematical Statistics Lecture Notes - Monograph Series (pp. 14–30). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215460490
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  - Clyde, M. A., House, L. L., Wolpert, R. L., & Vannucci, M. (n.d.). Nonparametric Models for Proteomic Peak Identification and Quantification. In Bayesian Inference for Gene Expression and Proteomics (pp. 293–308). Cambridge University Press. https://doi.org/10.1017/cbo9780511584589.016
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- Conference Papers
  - 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.
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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.
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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).
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- Software
  - Clyde, M. A. (2016). BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling. CRAN. https://doi.org/10.5281/zenodo.59497
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Teaching & Mentoring
Recent Courses
- MATH 743: Linear Models 2020
- STA 701S: Readings in Statistical Science 2020
- STA 721: Linear Models 2020
- STA 790-1: Special Topics in Statistics 2020
- STA 521L: Predictive Modeling and Statistical Learning 2019
Scholarly, Clinical, & Service Activities
Presentations & Appearances
- Model Uncertainty and Uncertainty Quantification. SIAM Uncertainty Quantification UQ18. SIAM. April 2018 2018
Service to the Profession
Service to Duke
- Duke University. Provost Committee on Tenure Standards. 2017 - 2018 2017 - 2018
- Faculty Compensation Committee. 2012 - 2018 2012 - 2018
- Duke University. Executive Committee of the Graduate Faculty. 2008 - 2012 2008 - 2012

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