On the Convergence of Monte Carlo Maximum Likelihood Calculations

On the Convergence of Monte Carlo Maximum Likelihood Calculations (1992)

by Charles J. Geyer

Venue:	Journal of the Royal Statistical Society B
Citations:	49 - 2 self

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Datum	Value	Source
TITLE	On the Convergence of Monte Carlo Maximum Likelihood Calculations	user correction - Legacy Corrections
AUTHOR NAME	Charles J. Geyer	SVM HeaderParse 0.1
AUTHOR AFFIL	; 1; School of Statistics; University of Minnesota; 1	SVM HeaderParse 0.2
ABSTRACT	Monte Carlo maximum likelihood for normalized families of distributions (Geyer and Thompson, 1992) can be used for an extremely broad class of models. Given any family f h ` : ` 2 \Theta g of nonnegative integrable functions, maximum likelihood estimates in the family obtained by normalizing the the functions to integrate to one can be approximated by Monte Carlo, the only regularity conditions being a compactification of the parameter space such that the the evaluation maps ` 7! h ` (x) remain continuous. Then with probability one the Monte Carlo approximant to the log likelihood hypoconverges to the exact log likelihood, its maximizer converges to the exact maximum likelihood estimate, approximations to profile likelihoods hypoconverge to the exact profile, and level sets of the approximate likelihood (support regions) converge to the exact sets (in Painlev'e-Kuratowski set convergence). The same results hold when there are missing data (Thompson and Guo, 1991, Gelfand and Carlin, 19...	user correction - Legacy Corrections
YEAR	1992	user correction - Legacy Corrections
VENUE	Journal of the Royal Statistical Society B	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	261--274	INFERENCE
VOLUME	56	INFERENCE
CITATIONS	34 found	ParsCit 1.0