title: Generalized Degrees of Freedom and Adaptive Model Selection in Linear Mixed-Effects Models.
abstract: Linear mixed-effects models involve fixed effects, random effects and covariance structure, which require model selection to simplify a model and to enhance its interpretability and predictability. In this article, we develop, in the context of linear mixed-effects models, the generalized degrees of freedom and an adaptive model selection procedure defined by a data-driven model complexity penalty. Numerically, the procedure performs well against its competitors not only in selecting fixed effects but in selecting random effects and covariance structure as well. Theoretically, asymptotic optimality of the proposed methodology is established over a class of information criteria. The proposed methodology is applied to the BioCycle study, to determine predictors of hormone levels among premenopausal women and to assess variation in hormone levels both between and within women across the menstrual cycle.
authors.affiliation: Division of Epidemiology, Statistics, and Prevention Research,  Eunice Kennedy Shriver  National Institute of Child Health and Human Development, Bethesda, MD 20892, U.S.A.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Shen
authors.forename: Sunni L
authors.initials: SL
authors.lastname: Mumford
references.citation: J Clin Endocrinol Metab. 2010 Sep;95(9):E80-5
references.pmid: 20534764
references.citation: Biometrics. 1982 Dec;38(4):963-74
references.pmid: 7168798
references.citation: Am J Epidemiol. 2010 Aug 15;172(4):430-9
references.pmid: 20679069
references.citation: Am J Epidemiol. 1996 Aug 15;144(4):381-8
references.pmid: 8712195
references.citation: Cancer. 2003 Nov 1;98(9):1870-9
references.pmid: 14584069
references.citation: Paediatr Perinat Epidemiol. 2009 Mar;23(2):171-84
references.pmid: 19159403
references.citation: J Biopharm Stat. 1997 Nov;7(4):481-500
references.pmid: 9358325
references.citation: Ann Stat. 2008 Aug 1;36(4):1509-1533
references.pmid: 19823597