JSM 2005 Online Program

JSM Activity #CE_03C

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

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CE_03C	Sat, 8/6/05, 8:00 AM - 4:00 PM	MCC-101 F
Bayesian Methods for Multivariate Regression: Variable Selection and Covariance Selection Models - Continuing Education - Course
ASA, Section on Bayesian Statistical Science
Instructor(s): Robert Kohn, University of New South Wales, Christopher K. Carter, CSIRO
Multivariate regression models are used in a wide variety of statistical applications. However, these models often have a large number of parameters included in both the mean term as well as the covariance matrix. It is therefore important to obtain parsimonious representations of these models by using variable selection and covariance selection methods. By covariance selection we mean methods that identify zeros in the off-diagonal elements of the covariance matrix or its inverse. This short course provides an introduction to both Bayesian variable selection models and Bayesian covariance selection models and addresses the following topics. Priors for variable selection models, priors for covariance selection models and their graphical model interpretation, efficient Markov chain Monte Carlo (MCMC) algorithms for sampling from the posterior and predictive distributions, estimation of normalizing constants by simulation, simultaneous estimation of mean and covariance parameters and Copula models for non-Gaussian multivariate data. The course presents a balance between theory and applications, and detailed examples are presented from business and biostatistics using software made available from the presenters. Overall, this course will give sufficient background for researchers to be knowledgeable users of a powerful class of Bayesian models in a variety of applications.