Conditional Akaike information criterion for generalized linear mixed models

Yu, Dalei; Yau, Kelvin K. W.

doi:10.1016/j.csda.2011.09.012

Cited by 31 publications

(47 citation statements)

References 25 publications

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“…In the particular case of models with random effects, the most interesting references are, chronologically ordered, Hodges and Sargent (), Vaida and Blanchard (), Greven and Kneib (), Yu and Yau (), Zhang et al . (), Muller et al .…”

Section: Introductionmentioning

confidence: 99%

Mixed Generalized Akaike Information Criterion for Small Area Models

Lombardía

López-Vizcaíno²,

Rueda

2017

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary A mixed generalized Akaike information criterion xGAIC is introduced and validated. It is derived from a quasi‐log‐likelihood that focuses on the random effect and the variability between the areas, and from a generalized degree‐of‐freedom measure, as a model complexity penalty, which is calculated by the bootstrap. To study the performance of xGAIC, we consider three popular mixed models in small area inference: a Fay–Herriot model, a monotone model and a penalized spline model. A simulation study shows the good performance of xGAIC. Besides, we show its relevance in practice, with two real applications: the estimation of employed people by economic activity and the prevalence of smokers in Galician counties. In the second case, where it is unclear which explanatory variables should be included in the model, the problem of selection between these explanatory variables is solved simultaneously with the problem of the specification of the functional form between the linear, monotone or spline options.

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Section: Introductionmentioning

confidence: 99%

Mixed Generalized Akaike Information Criterion for Small Area Models

Lombardía

López-Vizcaíno²,

Rueda

2017

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Vaida & Blanchard () proposed a conditional AIC criterion for comparing linear mixed models with different random effects structures, based on inference on the conditional likelihood. The concept was extended to also apply to GLMMs by both Yu & Yau () and Saefken et al . ().…”

Section: Methodsmentioning

confidence: 99%

Spatio‐phylogenetic multispecies distribution models

et al. 2014

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Summary1 Ecologists increasingly consider phylogenetic relatedness in both community composition and spatial arrangements in communities. Here we considered both the phylogenetic correlation between multiple species and the spatial correlation induced by unobserved spatial heterogeneity on multiple plots. For this analysis, we introduced phylogenetic spatial generalised linear mixed models (PSGLMMs), which are an extension of phylogenetic generalised linear mixed models (PGLMMs). 2 We used the framework of generalised linear array models to simultaneously model species and plot dimension. Such models have the potential to explain the correlation of the phylogenetic relationship of the observed species and of the spatial proximity of the plots, or both. We proposed model selection strategies based on proper scores and empirically evaluated them in a case study using bird count data. In our analysis, we focused on two special cases: the community composition model and the environmental sensitivity model. 3 Our simulation study indicated that it might be difficult to correctly identify phylogenetic signals when the phylogenetic correlation is rather low and when studying presence-absence or count data of rare or pervasive species.

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“…in Donohue et al . (), Yu and Yau () and Yu et al . ()) involve the form of probability density function of the random effects but cGIC does not.…”

Section: Model Selection In Generalized Linear Mixed Modelsmentioning

confidence: 99%

Asymptotic Properties and Information Criteria for Misspecified Generalized Linear Mixed Models

Zhang

Yau

2018

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback–Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate models are misspecified. The model selection consistency of the model selection procedure is also established when the true data‐generating procedure lies within the set of candidate models. Simulation experiments confirm the effectiveness of the method proposed. The use of the criterion for model selection is illustrated through an analysis of the European Currency Opinion Survey data.

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Conditional Akaike information criterion for generalized linear mixed models

Cited by 31 publications

References 25 publications

Mixed Generalized Akaike Information Criterion for Small Area Models

Mixed Generalized Akaike Information Criterion for Small Area Models

Spatio‐phylogenetic multispecies distribution models

Asymptotic Properties and Information Criteria for Misspecified Generalized Linear Mixed Models

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