Consistency of maximum likelihood estimators in general random effects models for binary data

DOI10.1214/aos/1034276633zbMath0897.62032OpenAlexW2020761100MaRDI QIDQ1355183

Thomas A. Louis, Steven M. Butler

Publication date: 19 October 1998

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

Full work available at URL: https://doi.org/10.1214/aos/1034276633

zbMATH Keywords

consistencyrandom effectsmixing distributionmaximum likelihood estimatorsidentifiabilityprobit modelsmixing distributionsgeneral random effects modellatent linear modelmixed logistic regressionrepeated binary measures

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Generalized linear models (logistic models) (62J12)

Related Items

Prediction of Random Effects in Linear and Generalized Linear Models under Model Misspecification, Strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models, Posterior consistency of random effects models for binary data, Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses, The role of conditional likelihoods in latent variable modeling

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Random-Effects Models for Longitudinal Data
• The geometry of mixture likelihoods: A general theory
• Consistency of maximum likelihood estimators for certain nonparametric families, in particular: Mixtures
• Existence and consistency of maximum likelihood in upgraded mixture models
• Identifiability of finite mixtures of logistic regression models
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• A Mixed-Model Procedure for Analyzing Ordered Categorical Data
• Semiparametric Estimation in the Rasch Model and Related Exponential Response Models, Including a Simple Latent Class Model for Item Analysis
• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Identifiability of Mixtures
• The identifiability of mixtures of distributions
• A Random Effects Model for Binary Data