JSM 2004 Online Program

Abstract #300110

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Activity Number:	202
Type:	Invited
Date/Time:	Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #300110
Title:	Bayesian Modeling of Several Covariance Matrices
Author(s):	Michael Daniels*+
Companies:	University of Florida
Address:	Dept. of Statistics, Gainesville, FL, 32611,
Keywords:
Abstract:	We explore simultaneous modeling of several covariance matrices using the spectral (eigenvalue) decomposition and modified Cholesky decomposition. We first assume the "dependence" matrices to be constant and the diagonal matrices of eigenvalues/innovation variances to be nonconstant, then log-linear models for the latter are used to reduce the number of free parameters to a manageable size. We explore the propriety of the posterior in such models with improper priors. We then explore an approach to weaken the assumption of constant dependence matrices in an automated fashion and describe how to compute Bayes Factors to test this hypothesis of constancy. The procedures are applied to data from two longitudinal clinical trials.

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