Abstract #300724

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JSM 2003 Abstract #300724
Activity Number: 104
Type: Invited
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300724
Title: Effective MCMC Sampling from Conditional Distributions of Log-Linear Models
Author(s): Brian Scott Caffo*+
Companies: Johns Hopkins University
Address: 615 N Wolfe St., Baltimore, MD, 21205-2103,
Keywords: log-linear ; p value ; contingency table ; Monte Carlo MCMC ; Fisher's exact test
Abstract:

Conditional inference eliminates nuisance parameters by conditioning on their sufficient statistics. For contingency tables conditional inference entails enumerating all tables with the same sufficient statistics as the observed data. For moderately sized tables and/or complex models the computing time to enumerate these tables is often prohibitive. Monte Carlo approximations offer a viable alternative provided it is possible to obtain samples from the correct conditional distribution. This talk presents a Markov chain Monte Carlo solution algorithm based on the Metropolis/Hatings/Green algorithm with a sequentially generated rounded normal candidate. The talk concludes with a discussion of general software implementation of these methods.

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Revised March 2003