Monte Carlo Likelihood Inference for Missing Data Models

We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observe...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:	The Annals of Statistics. - Institute of Mathematical Statistics. - 35(2007), 3, Seite 990-1011
1. Verfasser:	Sung, Yun Ju (VerfasserIn)
Weitere Verfasser:	Geyer, Charles J.
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2007
Zugriff auf das übergeordnete Werk:	The Annals of Statistics
Schlagworte:	Asymptotic theory Monte Carlo Maximum likelihood Generalized linear mixed model Empirical process Model misspecification Mathematics Information science Philosophy Physical sciences Behavioral sciences


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100	1		\|a Sung, Yun Ju \|e verfasserin \|4 aut
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520			\|a We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer θ* of the Kullback-Leibler information, as both Monte Carlo and observed data sample sizes go to infinity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing confidence regions for θ*. We give Logit-Normal generalized linear mixed model examples, calculated using an R package.
540			\|a Copyright 2007 Institute of Mathematical Statistics
650		4	\|a Asymptotic theory
650		4	\|a Monte Carlo
650		4	\|a Maximum likelihood
650		4	\|a Generalized linear mixed model
650		4	\|a Empirical process
650		4	\|a Model misspecification
650		4	\|a Mathematics \|x Applied mathematics \|x Analytics \|x Analytical estimating \|x Maximum likelihood estimation
650		4	\|a Information science \|x Information management \|x Data management \|x Data types \|x Missing data
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Descriptive statistics \|x Measures of variability \|x Sample size
650		4	\|a Mathematics \|x Pure mathematics \|x Geometry \|x Geometric shapes \|x Conic sections \|x Ellipses
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Descriptive statistics \|x Measures of variability \|x Statistical variance
650		4	\|a Mathematics \|x Mathematical procedures \|x Approximation
650		4	\|a Philosophy \|x Logic \|x Logical topics \|x Formal logic \|x Mathematical logic \|x Mathematical set theory \|x Transfinite numbers \|x Infinity
650		4	\|a Physical sciences \|x Physics \|x Mechanics \|x Density
650		4	\|a Behavioral sciences \|x Psychology \|x Cognitive psychology \|x Cognitive processes \|x Thought processes \|x Reasoning \|x Inference
650		4	\|a Information science \|x Information management \|x Data management \|x Data architecture \|x Data models
650		4	\|a Mathematics \|x Applied mathematics \|x Analytics \|x Analytical estimating \|x Maximum likelihood estimation
650		4	\|a Information science \|x Information management \|x Data management \|x Data types \|x Missing data
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Descriptive statistics \|x Measures of variability \|x Sample size
650		4	\|a Mathematics \|x Pure mathematics \|x Geometry \|x Geometric shapes \|x Conic sections \|x Ellipses
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Descriptive statistics \|x Measures of variability \|x Statistical variance
650		4	\|a Mathematics \|x Mathematical procedures \|x Approximation
650		4	\|a Philosophy \|x Logic \|x Logical topics \|x Formal logic \|x Mathematical logic \|x Mathematical set theory \|x Transfinite numbers \|x Infinity
650		4	\|a Physical sciences \|x Physics \|x Mechanics \|x Density
650		4	\|a Behavioral sciences \|x Psychology \|x Cognitive psychology \|x Cognitive processes \|x Thought processes \|x Reasoning \|x Inference
650		4	\|a Information science \|x Information management \|x Data management \|x Data architecture \|x Data models \|x Likelihood Based Inference
655		4	\|a research-article
700	1		\|a Geyer, Charles J. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t The Annals of Statistics \|d Institute of Mathematical Statistics \|g 35(2007), 3, Seite 990-1011 \|w (DE-627)270129162 \|w (DE-600)1476670-X \|x 00905364 \|7 nnns
773	1	8	\|g volume:35 \|g year:2007 \|g number:3 \|g pages:990-1011
856	4	0	\|u https://www.jstor.org/stable/25463589 \|3 Volltext
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