A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model

A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model (2001)

by James G. Booth , James P. Hobert , Wolfgang Jank

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Datum	Value	Source
TITLE	A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model	user correction - Legacy Corrections
AUTHOR NAME	James G. Booth	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Statistics; University of Florida	SVM HeaderParse 0.2
AUTHOR NAME	James P. Hobert	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Statistics; University of Florida	SVM HeaderParse 0.2
AUTHOR NAME	Wolfgang Jank	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Statistics; University of Florida	SVM HeaderParse 0.2
ABSTRACT	Likelihood inference with hierarchical models is often complicated by the fact that the likelihood function involves intractable integrals. Numerical integration (e.g. quadrature) is an option if the dimension of the integral is low but quickly becomes unreliable as the dimension grows. An alternative approach is to approximate the intractable integrals using Monte Carlo averages. Several dierent algorithms based on this idea have been proposed. In this paper we discuss the relative merits of simulated maximum likelihood, Monte Carlo EM, Monte Carlo Newton-Raphson and stochastic approximation. Key words and phrases : Eciency, Monte Carlo EM, Monte Carlo Newton-Raphson, Rate of convergence, Simulated maximum likelihood, Stochastic approximation All three authors partially supported by NSF Grant DMS-00-72827. 1 1	user correction - Legacy Corrections
YEAR	2001	user correction - Legacy Corrections
CITATIONS	32 found	ParsCit 1.0