Conditional and unconditional methods for selecting variables in linear mixed models

DOI10.1016/j.jmva.2010.11.007zbMath1207.62144OpenAlexW2016050619MaRDI QIDQ631631

Tatsuya Kubokawa

Publication date: 14 March 2011

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.11.007

zbMATH Keywords

prediction errorAkaike information criterionmaximum likelihood estimatorsmall area estimationlinear mixed modelrestricted maximum likelihood estimatorFay-Herriot modelbest linear unbiased predictornested error regression model

Mathematics Subject Classification ID

Related Items (9)

Information criteria for Fay-Herriot model selection ⋮ A graphical model selection tool for mixed models ⋮ Information based model selection criteria for generalized linear mixed models with unknown variance component parameters ⋮ Effective degrees of freedom and its application to conditional AIC for linear mixed-effects models with correlated error structures ⋮ Conditional conceptual predictive statistic for mixed model selection ⋮ Model selection in linear mixed-effect models ⋮ Modified conditional AIC in linear mixed models ⋮ Parametric bootstrap methods for bias correction in linear mixed models ⋮ Model selection in linear mixed models

Cites Work

• Unnamed Item
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• Estimation of the mean of a multivariate normal distribution
• Estimating the dimension of a model
• Mean squared error of empirical predictor.
• Fitting autoregressive models for prediction
• Statistical predictor identification
• Counting degrees of freedom in hierarchical and other richly-parameterised models
• A note on conditional AIC for linear mixed-effects models
• Maximum likelihood estimation of models for residual covariance in spatial regression
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Modified AIC and Cp in multivariate linear regression
• Generalised information criteria in model selection
• Bayesian Measures of Model Complexity and Fit
• On measuring the variability of small area estimators under a basic area level model
• Conditional Akaike information for mixed-effects models
• The Estimation of the Mean Squared Error of Small-Area Estimators
• A new look at the statistical model identification

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