Fixed and Random Effects Selection in Mixed Effects Models

Joseph G. Ibrahim, Hongtu Zhu, Ramon I. Garcia, Ruixin Guo

Research output: Contribution to journalArticlepeer-review

110 Scopus citations

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 ICQstatistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008,Journal of the American Statistical Association103, 1648-1658). The variable selection procedure based on ICQis 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.

Original languageEnglish (US)
Pages (from-to)495-503
Number of pages9
JournalBiometrics
Volume67
Issue number2
DOIs
StatePublished - Jun 2011

Keywords

  • ALASSO
  • Cholesky decomposition
  • EM algorithm
  • ICcriterion
  • Mixed effects selection
  • Penalized likelihood
  • SCAD

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics

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