Monte Carlo likelihood inference for missing data models

DOI10.1214/009053606000001389zbMath1124.62009arXiv0708.2184OpenAlexW3106418260MaRDI QIDQ2642738

Yun Ju Sung, Charles J. Geyer

Publication date: 4 September 2007

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0708.2184

zbMATH Keywords

maximum likelihoodempirical processasymptotic theorymodel misspecificationgeneralized linear mixed model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Generalized linear models (logistic models) (62J12) Monte Carlo methods (65C05)

Related Items

Reduced-Dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models, Local adaptation and genetic effects on fitness: calculations for exponential family models with random effects, An MCMC approach to empirical Bayes inference and Bayesian sensitivity analysis via empirical processes, Consistent maximum likelihood estimation using subsets with applications to multivariate mixed models, Gaussian copula marginal regression, A CLASSICAL INVARIANCE APPROACH TO THE NORMAL MIXTURE PROBLEM, Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event, Empirical likelihood for a partially linear model with covariate data missing at random, Asymptotics of maximum likelihood estimators based on Markov chain Monte Carlo methods, Likelihood‐based Inference with Missing Data Under Missing‐at‐Random

Uses Software

• R
• bernor

Cites Work

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