Scholars@Duke

• 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

  • 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
• Recognition
• 

  In the News

  • DEC 9, 2013

    Durham Herald-Sun

    Duke trustees approve six new graduate programs
• 

  Awards & Honors

  • 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
• 

  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
• 

  Selected Grants

  • 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
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Day, DB, Clyde, MA, Xiang, J, Li, F, Cui, X, Mo, J, Gong, J, Weschler, CJ, Zhang, Y, & Zhang, JJ. (2018, July). 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.  Full Text
    • Li, Y, & Clyde, MA. (2018, May 3). Mixtures of g -Priors in Generalized Linear Models. Journal of the American Statistical Association ,, 0-0.  Full Text  Open Access Copy
    • Day, DB, Xiang, J, Mo, J, Clyde, MA, Weschler, CJ, Li, F, Gong, J, Chung, M, Zhang, Y, & Zhang, J. (2018, May). 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.  Full Text
    • Benjamin, DJ, Berger, JO, Johannesson, M, Nosek, BA, Wagenmakers, E-J, Berk, R, Bollen, KA, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, CD, Clyde, M, Cook, TD, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, AP, Forster, M, George, EI, Gonzalez, R, Goodman, S, Green, E, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, DJ, Imai, K, Imbens, G, Ioannidis, JPA, Jeon, M, Jones, JH, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Munafó, M, Nakagawa, S, Nyhan, B, Parker, TH, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, FD, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, DJ, Winship, C, Wolpert, RL, Xie, Y, Young, C, Zinman, J, & Johnson, VE. (2018, January). Redefine statistical significance. Nature Human Behaviour , 2 (1), 6-10.  Full Text  Open Access Copy
    • (2017, July 1). 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”. American Journal of Epidemiology , 186 (1), 130-130.  Full Text
    • Clyde, MA, Palmieri Weber, R, Iversen, ES, Poole, EM, Doherty, JA, Goodman, MT, Ness, RB, Risch, HA, Rossing, MA, Terry, KL, Wentzensen, N, Whittemore, AS, Anton-Culver, H, Bandera, EV, Berchuck, A, Carney, ME, Cramer, DW, Cunningham, JM, Cushing-Haugen, KL, Edwards, RP, Fridley, BL, Goode, EL, Lurie, G, McGuire, V, Modugno, F, Moysich, KB, Olson, SH, Pearce, CL, Pike, MC, Rothstein, JH, Sellers, TA, Sieh, W, Stram, D, Thompson, PJ, Vierkant, RA, Wicklund, KG, Wu, AH, Ziogas, A, Tworoger, SS, & Schildkraut, JM. (2016, October 15). Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. American Journal of Epidemiology , 184 (8), 579-589.
    • Clyde, MA, Palmieri Weber, R, Iversen, ES, Poole, EM, Doherty, JA, Goodman, MT, Ness, RB, Risch, HA, Rossing, MA, Terry, KL, Wentzensen, N, Whittemore, AS, Anton-Culver, H, Bandera, EV, Berchuck, A, Carney, ME, Cramer, DW, Cunningham, JM, Cushing-Haugen, KL, Edwards, RP, Fridley, BL, Goode, EL, Lurie, G, McGuire, V, Modugno, F, Moysich, KB, Olson, SH, Pearce, CL, Pike, MC, Rothstein, JH, Sellers, TA, Sieh, W, Stram, D, Thompson, PJ, Vierkant, RA, Wicklund, KG, Wu, AH, Ziogas, A, Tworoger, SS, Schildkraut, JM, , & on behalf of the Ovarian Cancer Association Consortium, . (2016, October 3). Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. American Journal of Epidemiology , 184 (8), 579-589.  Full Text  Open Access Copy
    • Chou, ND, Serafini, S, Grant, GA, Clyde, M, Komisarow, J, & Muh, CR. (2016, August). 127 Multimodality Word-Finding Distinctions in Pediatric Cortical Stimulation Mapping. Neurosurgery , 63 Suppl 1 , 152-.  Full Text
    • Chou, ND, Serafini, S, Grant, GA, Clyde, M, Komisarow, J, & Muh, CR. (2016, August). 127 Multimodality Word-Finding Distinctions in Pediatric Cortical Stimulation Mapping. Neurosurgery , 63 (CN_suppl_1), 152-.
    • Iversen, ES, Lipton, G, Clyde, MA, & Monteiro, ANA. (2014, May 24). Functional annotation signatures of disease susceptibility loci improve SNP association analysis. BMC genomics , 15 , 398-.  Full Text  Open Access Copy
    • Serafini, S, Clyde, MA, Tolson, M, & Haglund, MM. (2013). Multimodality word-finding distinctions in cortical stimulation mapping. Neurosurgery , 73 (1), 36-47.  Full Text
    • Clyde, MA, & Ghosh, J. (2012). Finite population estimators in stochastic search variable selection. Biometrika , 99 (4), 981-988.  Full Text  Open Access Copy
    • Loredo, TJ, Berger, JO, Chernoff, DF, Clyde, MA, & Liu, B. (2012). Bayesian methods for analysis and adaptive scheduling of exoplanet observations. Statistical Methodology , 9 (1-2), 101-114.  Full Text
    • Armagan, A, Dunson, DB, & Clyde, MA. (2011). Generalized Beta Mixtures of Gaussians. (Shawe-Taylor, J, Zemel, RS and Bartlett, PL, (Eds.). Advances in Neural Information Processing Systems , 24 , 523-531.
    • Clyde, MA, Ghosh, J, & Littman, ML. (2011, January). Bayesian Adaptive Sampling for Variable Selection and Model Averaging. Journal of Computational and Graphical Statistics , 20 (1), 80-101.  Full Text
    • Ghosh, J, & Clyde, MA. (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.  Full Text
    • Wolpert, RL, Clyde, MA, & Tu, C. (2011). Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels. Annals of Statistics , 39 (4), 1916-1962.  Full Text  Open Access Copy
    • Wilson, MA, Iversen, ES, Clyde, MA, Schmidler, SC, & Schildkraut, JM. (2010, September). Bayesian model search and multilevel inference for SNP association studies. The Annals of Applied Statistics , 4 (3), 1342-1364.  Full Text  Open Access Copy
    • Schildkraut, JM, Iversen, ES, Wilson, MA, Clyde, MA, Moorman, PG, Palmieri, RT, Whitaker, R, Bentley, RC, Marks, JR, & Berchuck, A. (2010, April 8). Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer.(Published online) Plos One , 5 (4), e10061-null.  Full Text  Open Access Copy  Link to Item
    • Jesneck, JL, Mukherjee, S, Yurkovetsky, Z, Clyde, M, Marks, JR, Lokshin, AE, & Lo, JY. (2009, May 28). Do serum biomarkers really measure breast cancer?(Published online). Bmc Cancer , 9 , 164-null.  Full Text  Link to Item
    • Schildkraut, J, Iversen, E, Marks, J, Wilson, M, Clyde, M, Palmieri, R, Moorman, P, & Berchuck, A. (2009, May 1). Association between serous invasive ovarian cancer and variants in candidate DNA damage response genes. CANCER RESEARCH , 69 .  Link to Item
    • Schildkraut, JM, Goode, EL, Clyde, MA, Iversen, ES, Moorman, PG, Berchuck, A, Marks, JR, Lissowska, J, Brinton, L, Peplonska, B, Cunningham, JM, Vierkant, RA, Rider, DN, Chenevix-Trench, G, Webb, PM, Beesley, J, Chen, X, Phelan, C, Sutphen, R, Sellers, TA, Pearce, L, Wu, AH, Van Berg, DD, Conti, D, Elund, CK, Anderson, R, Goodman, MT, Lurie, G, Carney, ME, Thompson, PJ, Gayther, SA, Ramus, SJ, Jacobs, I, Kjaer, SK, Hogdall, E, Blaakaer, J, Hogdall, C, Easton, DF, Song, H, Pharoah, PDP, Whittemore, AS, McGuire, V, Quaye, L, Anton-Culver, H, Ziogas, A, Terry, KL, Cramer, DW, Hankinson, SE, Tworoger, SS, Calingaert, B, Chanock, S, Sherman, M, & Garcia-Closas, M. (2009, March 15). Single nucleotide polymorphisms in the TP53 region and susceptibility to invasive epithelial ovarian cancer. Cancer Research , 69 (6), 2349-2357.  Full Text
    • Chu, JH, Clyde, MA, & Liang, F. (2009). Bayesian function estimation using continuous wavelet dictionaries. Statistica Sinica , 19 (4), 1419-1438.  Link to Item
    • Zhou, XK, Clyde, MA, Garrett, J, Lourdes, V, O'Connell, M, Parmigiani, G, Turner, DJ, & 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.  Full Text
    • Palmieri, RT, Wilson, MA, Iversen, ES, Clyde, MA, Calingaert, B, Moorman, PG, Poole, C, Anderson, AR, Anderson, S, Anton-Culver, H, Beesley, J, Hogdall, E, Brewster, W, Carney, ME, Chen, X, Chenevix-Trench, G, Chang-Claude, J, Cunningham, JM, Dicioccio, RA, Doherty, JA, Easton, DF, Edlund, CK, Gayther, SA, Gentry-Maharaj, A, Goode, EL, Goodman, MT, Kjaer, SK, Hogdall, CK, Hopkins, MP, Jenison, EL, Blaakaer, J, Lurie, G, McGuire, V, Menon, U, Moysich, KB, Ness, RB, Pearce, CL, Pharoah, PDP, Pike, MC, Ramus, SJ, Rossing, MA, Song, H, Terada, KY, Vandenberg, D, Vierkant, RA, Wang-Gohrke, S, Webb, PM, Whittemore, AS, Wu, AH, Ziogas, A, Berchuck, A, Schildkraut, JM, Ovarian Cancer Association Consortium, , Australian Cancer Study (Ovarian Cancer Group), , & Australian Ovarian Cancer Study Group, . (2008, December). Polymorphism in the IL18 gene and epithelial ovarian cancer in non-Hispanic white women. Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology , 17 (12), 3567-3572.  Full Text
    • Liang, F, Paulo, R, Molina, G, Clyde, MA, & Berger, JO. (2008, March). Mixtures of g Priors for Bayesian Variable Selection. Journal of the American Statistical Association , 103 (481), 410-423.  Full Text
    • Clyde, MA, Berger, JO, Bullard, F, Ford, EB, Jefferys, WH, Luo, R, Paulo, R, & Loredo, T. (2007, January 1). Current challenges in Bayesian model choice. (Babu, GJ and Feigelson, ED, (Eds.). Statistical Challenges in Modern Astronomy Iv , 371 , 224-+.  Link to Item
    • Kuncel, AM, Cooper, SE, Wolgamuth, BR, Clyde, MA, Snyder, SA, Montgomery, EB, Rezai, AR, & Grill, WM. (2006, November). 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.  Full Text
    • Clyde, M, House, L, Tu, C, & Wolpert, RL. (2005). Bayesian Nonparametric Function Estimation Using Overcomplete Representations and Levy Random Field Priors. JO Berger and H Dette (Eds.). Oberwolfach Reports , 2 (4), 2628-2633.  Full Text
    • House, L, Clyde, MA, & Huang, YC. (2005). Bayesian Identification of Differential Gene Expression Induced by Metals in Human Bronchial Epithelial Cells. Bayesisan Analysis , 1 (1), 105-120.  Full Text
    • Goodman, AM, Clyde, MA, Burdick, DS, Idriss, SF, & Wolf, PD. (2004, April). Minimum energy single-shock internal atrial defibrillation in sheep. J Interv Card Electrophysiol , 10 (2), 131-138.  Full Text  Link to Item
    • George, EI, & Clyde, M. (2004, February). Model Uncertainty. Statistical Science , 19 (1), 81-94.  Full Text
    • Lee, HKH, & Clyde, MA. (2004). Lossless online Bayesian bagging. Journal of Machine Learning Research , 5 , 143-151.  Open Access Copy  Link to Item
    • Clyde, MA, & George, EI. (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.  Full Text
    • Dominici, F, Sheppard, L, & Clyde, M. (2003). Health effects of air pollution: A statistical review. International Statistical Review , 71 (2), 243-276.
    • 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.  Full Text
    • Clyde, M, & Lee, HK. (2001). Bagging and the Bayesian Bootstrap. T Richardson and T Jaakkola (Eds.). Artificial Intelligence and Statistics , 8 , 169-174.  Open Access Copy  Link to Item
    • Clyde, M. (2000, December 1). Model uncertainty and health effect studies for particulate matter. Environmetrics , 11 (6), 745-763.  Full Text
    • Clyde, M, & George, EI. (2000, November). Flexible empirical Bayes estimation for wavelets. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , 62 (4), 681-698.  Full Text
    • Dominici, F, Parmigiani, G, & Clyde, M. (2000). Conjugate analysis of multivariate normal data with incomplete observations. Canadian Journal of Statistics , 28 (3), 533-550.
    • Lamon, EC, & Clyde, MA. (2000). Accounting for model uncertainty in prediction of chlorophyll a in Lake Okeechobee. Journal of Agricultural, Biological, and Environmental Statistics , 5 (3), 297-322.
    • Maceachern, SN, Clyde, M, & Liu, JS. (1999, June). Sequential importance sampling for nonparametric Bayes models: The next generation. Canadian Journal of Statistics , 27 (2), 251-267.  Full Text
    • Clyde, M. (1999, January 1). Comment. Statistical Science , 14 (4), 401-404.
    • Clyde, MA, & Clyde, MA. (1999). Comment on ``Bayesian Model Averaging: A Tutorial'' by Hoeting, JA., Madigan,D., Raftery, AE., and Volinsky, CT.Comment on ``Bayesian model averaging: A tutorial'' (PublishedPublished). Statistical Science , 14 , 401-404. (Scholarly Commentary)  Full Text  Open Access Copy
    • Clyde, M. (1998, June 1). Multiple shrinkage and subset selection in wavelets. Biometrika , 85 (2), 391-401.  Full Text
    • Clyde, MA, & Parmigiani, G. (1998). Protein construct storage: Bayesian variable selection and prediction with mixtures. Journal of Biopharmaceutical Statistics , 8 (3), 431-443.
    • Clyde, MA. (1997). Strategies for Model Mixing in Generalized Linear Models. P Smyth and D Madigan (Eds.). Artificial Intelligence and Statistics , 6 , 103-114.
    • Clyde, MA. (1997). Strategies for Model Mixing in Generalized Linear Models. P Smyth and D Madigan (Eds.). 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.
    • Clyde, MA, DeSimone, H, & Parmigiani, G. (1996). Prediction via orthogonalized model mixing. Journal of the American Statistical Association , 91 , 1197-1208.  Full Text
    • Furnier, GR, Stine, M, Mohn, CA, & Clyde, MA. (1991). Geographic patterns of variation in allozymes and height growth in white spruce. Canadian Journal of Forest Research , 21 (5), 707-712.
    • Clyde, MA. (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. (Academic Article)
    • Clyde, MA. (1987). A new computerized system for tree ring measurement and analysis. Forestry Chronicle , 63 , 23-27. (Academic Article)
    • Furnier, GR, Knowles, P, Clyde, MA, & Dancik, BP. (1987). Effects of avian seed dispersal on the genetic structure of whitebark pine populations. Evolution , 41 (3), 607-612.
    • House, LL, Clyde, MA, & Wolpert, RL. Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy. Annals of Applied Statistics , 5 (2B), 1488-1511.  Full Text  Open Access Copy  Link to Item

  • Software

    • Clyde, MA. (2016). BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling.  Full Text  Link to Item

  • Book Sections

    • Clyde, MA. (2015, March 26). Experimental Design: Bayesian Designs. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 521-526).  Full Text
    • Clyde, MA. (2015). Experimental Design: Bayesian Designs. In JD Wright (Ed.), International Encyclopedia of Social and Behavioral Sciences (pp. 521-526). Science Direct. (Chapter)  Full Text  Link to Item
    • Clyde, MA. (2015). Experimental Design: Bayesian Designs. In JD Wright (Ed.), International Encyclopedia of Social and Behavioral Sciences (pp. 521-526). Science Direct. (Chapter)  Full Text  Link to Item
    • Serafini, S, Clyde, MA, Husain, AM, & Haglund, MM. (2015). Brain Mapping and Monitoring. In AM Husain (Ed.), Practical Epilepsy (pp. 192-199). Demos Medical Publishing, LLC. (Chapter)  Link to Item
    • Clyde, MA, & Iversen, ES. (2013). Bayesian Model Averaging in the M-Open Framework. In P Damien, P Dellaportas, NG Polson & DA Stephens (Eds.), Bayesian Theory and Applications (pp. 484-498). Oxford University Press. (Chapter)  Full Text  Open Access Copy
    • Clyde, MA, & Wolpert, RL. (2011). Discussion of ``Polson and Scott: Shrink globally, act locally: Sparse Bayesian regularization and prediction''. In JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith & M West (Eds.), Bayesian Statistics 9 (pp. 528-529). Oxford University Press.
    • Clyde, MA, & Wolpert, RL. (2011). Discussion of ``Polson and Scott: Shrink globally, act locally: Sparse Bayesian regularization and prediction''. In JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith & M West (Eds.), Bayesian Statistics 9 (pp. 528-529). Oxford University Press.
    • Clyde, MA, & Wolpert, RL. (2007). Nonparametric Function Estimation using Overcomplete Dictionaries (with Discussion). In JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith & M West (Eds.), Bayesian Statistics 8 (pp. 91-114). Oxford University Press.
    • Clyde, MA, & Wolpert, RL. (2007). Nonparametric Function Estimation using Overcomplete Dictionaries (with Discussion). In JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith & M West (Eds.), Bayesian Statistics 8 (pp. 91-114). Oxford University Press.
    • Clyde, M, House, L, & Wolpert, RL. (2006). Nonparametric Models for Proteomic Peak Identification and Quantification. In KA Do, P Muller & M Vannucci (Eds.), Bayesian Inference for Gene Expression and Proteomics (pp. 293-308). Cambridge University Press. (Chapter)  Full Text
    • Clyde, MA, House, L, & Wolpert, RL. (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. (Chapter)
    • Clyde, M. (2003). Model Averaging. In SJ Press (Ed.), Subjective and Objective Bayesian Statistics: Principles, Models, and Applications John Wiley & Sons. (Chapter)
    • Clyde, M. (2003). Invited discussion of "Bayesian and Frequentist Multiple Testing, by C. Genovese and L. Wasserman". In JM Bernardo, MJ Bayarri, AP Dawid, JO Berger, D Heckerman, AFM Smith & M West (Eds.), Bayesian Statistics 7 (pp. 157-160). Oxford University Press. (Chapter)
    • 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 (pp. 117-124). (Chapter)  Full Text  Open Access Copy
    • Clyde, MA. (2001). Experimental Design: Bayesian Designs. In NJ Smelser and PB Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences (pp. 5075-5081). New York, NY: Science Direct. (Chapter)  Full Text  Link to Item
    • Clyde, MA, & George, EI. (1999). Empirical Bayes estimation in wavelet nonparametric regression. In P Muller and B Vidakovic (Eds.), Bayesian Inference in Wavelet-Based Models (pp. 309-322). Springer. (Chapter)  Full Text  Link to Item
    • Clyde, MA. (1999). Bayesian Model Averaging and Model Search Strategies (with Discussion). In(Bernardo, JM, Dawid, AP, Berger, JO and Smith, AFM, (Eds.) Bayesian Statistics 6 (pp. 157-185) Oxford University Press.  Link to Item
    • Paddock, S, Clyde, MA, & West, M. (1998). Mixture models in the exploration of structure-activity relationships in drug design. In C Gastonis, RE 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. (Chapter)  Full Text
    • Clyde, MA, Parmigiani, G, & Vidakovic, B. (1997). Using Markov chain Monte Carlo to account for model uncertainty, with applications to wavelets. In L Billard and NI Fisher (Eds.), Computing science and statistics (pp. 209-218). Fairfax Station, VA: Interface Foundation of North America,. (Chapter)
    • Clyde, MA, Parmigiani, G, & Vidakovic, B. (1997). Using Markov chain Monte Carlo to account for model uncertainty, with applications to wavelets. In L Billard and NI Fisher (Eds.), Computing science and statistics (pp. 209-218). Fairfax Station, VA: Interface Foundation of North America,. (Chapter)
    • Clyde, MA, & Parmigiani, G. (1996). Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing. In JC Lee, WO Johnson & A Zellner (Eds.), Modelling and Prediction: Honoring Seymour Geisser (pp. 206-227). Springer. (Chapter)  Full Text
    • Clyde, MA, Muller, P, & Parmigiani, G. (1996). Inference and design strategies for a hierarchical logistic regression model. In DA Berry and DK Stangl (Eds.), Bayesian Biostatistics (pp. 297-320). New York, NY: Marcel Dekker. (Chapter)  Link to Item
    • Clyde, MA, Muller, P, & Parmigiani, G. (1996). Inference and design strategies for a hierarchical logistic regression model. In DA Berry and DK Stangl (Eds.), Bayesian Biostatistics (pp. 297-320). New York, NY: Marcel Dekker. (Chapter)  Link to Item
    • 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 JO Berger, JM Bernardo, AP Dawid & AFM Smith (Eds.), Bayesian Statistics 5 (pp. 341-341). Oxford University Press. (Chapter)
    • Clyde, MA, Muller, P, & Parmigiani, G. (1995). Optimal Design for Heart Defibrillators. In C Gastonia, JS Hodges, RE Kass & ND Singpurwall (Eds.), Bayesian Statistics in Science and Engineering: Case Studies II (pp. 278-292). Springer-Verlag. (Chapter)  Full Text  Link to Item
    • Clyde, MA. (1995). Bayesian Designs for Approximate Normality. In CP Kitsos and WG Muller (Eds.), MODA 4 -- Advances in Model--Oriented Data Analysis (pp. 25-35). Physica-Verlag. (Chapter)  Full Text  Link to Item
    • Clyde, MA. (1991). Logistic regression for spatial pair-potential models. In Spatial statistics and imaging (pp. 14-30). Institute of Mathematical Statistics. (Chapter)  Full Text
    • Clyde, MA. (1991). Logistic regression for spatial pair-potential models. In Spatial statistics and imaging (pp. 14-30). Institute of Mathematical Statistics. (Chapter)  Full Text

  • Conference Papers

    • Ness, R, Clyde, M, Palmieri, R, & Schildkraut, J. (2017, March). Risk prediction for ovarian cancer: epidemiologic risk factors plus confirmed genetic loci.  Link to Item
    • Clyde, MA, Weber, RP, Ness, R, & Schildkraut, J. (2016, October). RISK PREDICTION FOR OVARIAN CANCER: EPIDEMIOLOGIC RISK FACTORS PLUS CONFIRMED GENETIC LOCI.  Link to Item
    • Clyde, MA, DeSimone, H, & Parmigiani, G. (1994). A Comparison of Algorithms for Sampling Models. Joint Statistics Meetings.  Link to Item
• Teaching & Mentoring
• 

  Recent Courses

  • MATH 743: Linear Models 2018
  • STA 521L: Modern Regression and Predictive Modeling 2018
  • STA 721: Linear Models 2018
  • STA 493: Research Independent Study 2016
  • STA 643: Modern Design of Experiments 2016
  • STA 701S: Readings in Statistical Science 2016
• Scholarly, Clinical, & Service Activities
• 

  Presentations & Appearances

  • Model Uncertainty and Uncertainty Quantification. SIAM Uncertainty Quantification UQ18. SIAM. April 2018  2018
• 

  Service to the Profession

  • Chair. Section on Bayesian Statistical Science. American Statistical Association. 2018 - 2018  2018
  • President. International Society for Bayesian Analysis. 2011 - 2013  2011 - 2013
  • Executive Secretary. International Society for Bayesian Analysis. 2009 - 2011  2009 - 2011
• 

  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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