Conditional information criteria for selecting variables in linear mixed models
نویسندگان
چکیده
منابع مشابه
Conditional information criteria for selecting variables in linear mixed models
In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained frommany clusters. As the variable selection procedure, we here use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC...
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In linear mixed models, the Akaike information criterion (AIC) is often used to decide on the inclusion of a random effect. An important special case is the choice between linear and nonparametric regression models estimated using mixed model penalized splines. We investigate the behavior of two commonly used versions of the AIC, derived either from the implied marginal model or the conditional...
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In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for variable selection in light of the prediction of specific clusters or random effects. This is useful in problems involving prediction of random effects such as small area estimation, and much attention has been received since suggested by Vaida and Blanchard (2005). A weak point of cAIC is that it is ...
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We propose a method of inference for generalized linear mixed models Ž . GLMM that in many ways resembles the method of least squares. We also show that adequate inference about GLMM can be made based on the conditional likelihood on a subset of the random effects. One of the important features of our methods is that they rely on weak distributional assumptions about the random effects. The met...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2010
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2010.05.007