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National Institute of Environmental Health Sciences

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Publication Detail

Title: Fixed and random effects selection in mixed effects models.

Authors: Ibrahim, Joseph G; Zhu, Hongtu; Garcia, Ramon I; Guo, Ruixin

Published In Biometrics, (2011 Jun)

Abstract: We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model selection criterion, called the IC(Q) statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648-1658). The variable selection procedure based on IC(Q) is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from a Yale infant growth study are used to illustrate the proposed methodology.

PubMed ID: 20662831 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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