DEPARTMENT OF
APPLIED MATHEMATICS & STATISTICS
(AMS)
Reading 2016, Day 3: Bayesian Hierarchical Modeling
DEPARTMENT OF
APPLIED MATHEMATICS & STATISTICS
(AMS)
Reading 2016, Day 3: Bayesian Hierarchical Modeling
Topics will include Bayesian fixed- and random-effects meta-analysis, hierarchical modeling with latent variables as an approach to mixture modeling, prior distributions in Bayesian hierarchical modeling, Bayesian fitting of random-effects and mixed models, the analysis of cluster samples, and a comparison of likelihood-based and Bayesian methods for fitting hierarchical models: circumstances in which likelihood-based fitting can be poorly calibrated.
The case studies will be drawn from gerontology (analysis of a randomized controlled experiment to measure the effect of a health intervention on hospitalization rates for elderly non-institutionalized people), education (a meta-analysis of the effects of teacher expectancy on pupil performance; a variance-components model of student performance on mathematics tests, based on a single-stage cluster sample) and medicine (a meta-analysis of the effects of aspirin on mortality for heart attack patients; a random-effects logistic regression model to study rates of Western-style versus traditional prenatal care for mothers giving birth in Guatemala).
Through a series of practicals (computer labs), the course will liberally illustrate user-friendly implementations of MCMC sampling via the freeware programs WinBUGS (for Windows platforms) and rjags (for all platforms) when closed-form solutions are not possible.
The course is intended mainly for people who often use statistics in their research or other work in academia, government or industry; as noted above, a first course in Bayesian analysis equivalent to the content of days 1 and 2 of this five-day course will provide sufficient background for participants.
He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association (ASA), the Institute of Mathematical Statistics, and the Royal Statistical Society; from 2001 to 2003 he served as the President-Elect, President, and Past President of the International Society for Bayesian Analysis (ISBA).
He is the author or co-author of about 140 contributions to the methodological and applied statistical literature, including articles in the Journal of the Royal Statistical Society (Series A, B and C), the Journal of the American Statistical Association, the Annals of Applied Statistics, Bayesian Analysis, Statistical Science, the New England Journal of Medicine, and the Journal of the American Medical Association; his 1995 JRSS-B article on assessment and propagation of model uncertainty has been cited about 1,500 times, and taken together his publications have been cited about 13,200 times.
His research is in the areas of Bayesian inference and prediction, model uncertainty and empirical model-building, hierarchical modeling, Markov Chain Monte Carlo methods, and Bayesian nonparametric methods, with applications mainly in medicine, health policy, education, environmental risk assessment and data science.
His short courses have received Excellence in Continuing Education Awards from the American Statistical Association on two occasions. He has won or been nominated for major teaching awards everywhere he has taught (the University of Chicago; the RAND Graduate School of Public Policy Studies; the University of California, Los Angeles; the University of Bath (UK); and the University of California, Santa Cruz).
He has a particular interest in the exposition of complex statistical methods and ideas in the context of real-world applications.