Journal
BIOMETRICS
Volume 67, Issue 2, Pages 495-503Publisher
WILEY
DOI: 10.1111/j.1541-0420.2010.01463.x
Keywords
ALASSO; Cholesky decomposition; EM algorithm; ICQ criterion; Mixed effects selection; Penalized likelihood; SCAD
Funding
- NSF [BCS-08-26844]
- NIH [GM 70335, CA 74015, RR025747-01, MH086633, AG033387, P01CA142538-01]
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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 ICQ 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 ICQ 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.
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