title: Fixed and Random Effects Selection by REML and Pathwise Coordinate Optimization.
abstract: We propose a two-stage model selection procedure for the linear mixed-effects models. The procedure consists of two steps: First, penalized restricted log-likelihood is used to select the random effects, and this is done by adopting a Newton-type algorithm. Next, the penalized log-likelihood is used to select the fixed effects via pathwise coordinate optimization to improve the computation efficiency. We prove that our procedure has the oracle properties. Both simulation studies and a real data example are carried out to examine finite sample performance of the proposed fixed and random effects selection procedure. Supplementary materials including R code used in this article and proofs for the theorems are available online.
authors.affiliation: Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371.
authors.forename: Bingqing
authors.initials: B
authors.lastname: Lin
authors.affiliation: Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Pang
authors.affiliation: Department of Statistics, University of California, Davis, CA 85616.
authors.forename: Jiming
authors.initials: J
authors.lastname: Jiang
keywords: BIC; LASSO; Mixed-effects models