A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models

DOI10.1214/14-EJS881zbMath1282.62168WikidataQ56390566 ScholiaQ56390566MaRDI QIDQ2441054

Sonja Greven, Thomas Kneib, Benjamin Säfken, Clara-Sophie van Waveren

Publication date: 21 March 2014

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1393510264

zbMATH Keywords

model selectionrandom effectsKullback-Leibler distance

Mathematics Subject Classification ID

Related Items (9)

A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure ⋮ Longitudinal modeling of age-dependent latent traits with generalized additive latent and mixed models ⋮ A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models ⋮ Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio ⋮ Bias-corrected Kullback-Leibler distance criterion based model selection with covariables missing at random ⋮ Selection Strategy for Covariance Structure of Random Effects in Linear Mixed-effects Models ⋮ Selective inference for additive and linear mixed models ⋮ Model averaging for linear mixed models via augmented Lagrangian ⋮ Mixed-effects modelling for crossed and nested data: an analysis of dengue fever in the state of Goiás, Brazil

Uses Software

• SemiPar
• gamair

Cites Work

• Conditional Akaike information criterion for generalized linear mixed models
• Poisson approximation for dependent trials
• A natural identity for exponential families with applications in multiparameter estimation
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• A note on conditional Akaike information for Poisson regression with random effects
• A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models
• Counting degrees of freedom in hierarchical and other richly-parameterised models
• On the behaviour of marginal and conditional AIC in linear mixed models
• Conditional Akaike information under generalized linear and proportional hazards mixed models
• A note on conditional AIC for linear mixed-effects models
• Regression and time series model selection in small samples
• Semiparametric Regression
• Approximate Inference in Generalized Linear Mixed Models
• Conditional Akaike information for mixed-effects models
• Optimal Model Assessment, Selection, and Combination
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