A note on BIC in mixed-effects models

DOI10.1214/14-EJS890zbMath1348.62186MaRDI QIDQ2452101

Marc Lavielle, Marie-Anne Poursat, Maud Delattre

Publication date: 30 May 2014

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1399035845

zbMATH Keywords

Bayesian information criterion (BIC)variable selectionmixed effects model

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) General nonlinear regression (62J02)

Related Items (14)

Robust probabilistic classification applicable to irregularly sampled functional data ⋮ Consistent Fixed-Effects Selection in Ultra-high dimensional Linear Mixed Models with Error-Covariate Endogeneity ⋮ Estimation and selection in linear mixed models with missing data under compound symmetric structure ⋮ A SAEM algorithm for fused Lasso penalized nonlinear mixed effect models: application to group comparison in pharmacokinetics ⋮ A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure ⋮ A joint quantile regression model for multiple longitudinal outcomes ⋮ Co-clustering of spatially resolved transcriptomic data ⋮ A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data ⋮ Selection Strategy for Covariance Structure of Random Effects in Linear Mixed-effects Models ⋮ Mixed-effects estimation in dynamic models of plant growth for the assessment of inter-individual variability ⋮ Detecting multiple random changepoints in Bayesian piecewise growth mixture models ⋮ Fast selection of nonlinear mixed effect models using penalized likelihood ⋮ Co-clustering of time-dependent data via the shape invariant model ⋮ Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics

Uses Software

• R
• S-PLUS
• nlme
• SAS
• saemix
• MEMSS
• SAS/STAT

Cites Work

• Fence methods for mixed model selection
• Random-Effects Models for Longitudinal Data
• Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm
• Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models
• Maximum likelihood estimation in nonlinear mixed effects models
• Estimating the dimension of a model
• Convergence of a stochastic approximation version of the EM algorithm
• A note on conditional Akaike information for Poisson regression with random effects
• Fixed and random effects selection in nonparametric additive mixed models
• Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications
• Strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models
• Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models
• Conditional Akaike information under generalized linear and proportional hazards mixed models
• A note on conditional AIC for linear mixed-effects models
• A note on the use of Laplace's approximation for nonlinear mixed-effects models
• Mixed-Effects Models in S and S-PLUS
• Conditional Akaike information for mixed-effects models
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