JSM 2005 Online Program

Abstract #304242

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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Activity Number:	265
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	IMS
Abstract - #304242
Title:	Monte Carlo Methods for Maximizing Intractable Likelihood Functions
Author(s):	Ronald Neath*+ and Galin Jones
Companies:	University of Minnesota and University of Minnesota
Address:	School of Statistics, Minneapolis, MN, 55455, United States
Keywords:	Generalized linear mixed model ; Hierarchical model ; Markov chain Monte Carlo ; Maximum likelihood ; Monte Carlo EM ; Monte Carlo Newton-Raphson
Abstract:	Statistical models with a hierarchical structure, such as generalized linear mixed models, often yield likelihood functions involving high-dimensional, intractable integrals. Deterministic optimization algorithms, such as Newton-Raphson or EM, typically will be unsuitable in these instances. Among the Monte Carlo algorithms available are Monte Carlo EM (MCEM), Monte Carlo Newton-Raphson (MCNR), and Monte Carlo Likelihood Analysis (MCLA). We give a brief survey of these algorithms, emphasizing critical analysis of their performance in both toy and real examples. Some interesting connections between the algorithms also will be pointed out. Finally, we introduce the notion of automated algorithms and report on our efforts to automate MCLA.

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