Fixed and Random Effects Selection in Linear and Logistic Models

DOI10.1111/j.1541-0420.2007.00771.xzbMath1147.62022OpenAlexW2166576847WikidataQ51627083 ScholiaQ51627083MaRDI QIDQ5434894

S. K. Kinney, David B. Dunson

Publication date: 14 January 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00771.x

zbMATH Keywords

logistic regressionBayesian model selectionvariable selectionmodel averagingparameter expansionmixed effects modelvariance components test

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

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Cites Work

• Unnamed Item
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• Random-Effects Models for Longitudinal Data
• Bayesian reduced rank regression in econometrics
• Nonparametric regression using Bayesian variable selection
• Model uncertainty
• General design Bayesian generalized linear mixed models
• Order-restricted score tests for homogeneity in generalised linear and nonlinear mixed models
• Sequential Ordinal Modeling with Applications to Survival Data
• The Use of Score Tests for Inference on Variance Components
• Random Effects Selection in Linear Mixed Models
• Bayesian Multivariate Logistic Regression
• On scale mixtures of normal distributions
• Analysis of multivariate probit models
• Parameter expansion to accelerate EM: the PX-EM algorithm
• Random-Effects Models for Longitudinal Data Using Gibbs Sampling
• Variance component testing in generalised linear models with random effects
• The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models
• Theory & Methods: Bayesian variable selection in logistic regression: predicting company earnings direction
• Efficiency and Convergence Properties of Slice Samplers
• Bayes Factors and Approximations for Variance Component Models
• Parameter Expansion for Data Augmentation
• Likelihood Ratio Tests in Linear Mixed Models with One Variance Component
• Approximate Inference in Generalized Linear Mixed Models
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Covariance Selection in Generalized Linear Mixed Models
• Monte Carlo sampling methods using Markov chains and their applications
• Model determination for the variance component model using reference priors
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)