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.
Original language | English |
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Pages (from-to) | 341-355 |
Number of pages | 15 |
Journal | Journal of Computational and Graphical Statistics |
Volume | 22 |
Issue number | 2 |
DOIs | |
Publication status | Published - 17 Dec 2013 |
Externally published | Yes |
Keywords
- BIC
- LASSO
- Mixed-effects models
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Statistics and Probability
- Statistics, Probability and Uncertainty