New variable selection for linear mixed-effects models

Ping Wu, Xinchao Luo, Peirong Xu, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)

Abstract

In this paper, we consider how to select both the fixed effects and the random effects in linear mixed models. To make variable selection more efficient for such models in which there are high correlations between covariates associated with fixed and random effects, a novel approach is proposed, which orthogonalizes fixed and random effects such that the two sets of effects can be separately selected with less influence on one another. Also, unlike most of existing methods with parametric assumptions, the new method only needs fourth order moments of involved random variables. The oracle property is proved. the performance of our method is examined by a simulation study.

Original languageEnglish
Pages (from-to)627-646
Number of pages20
JournalAnnals of the Institute of Statistical Mathematics
Volume69
Issue number3
DOIs
Publication statusPublished - 1 Jun 2017

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Fixed and random effects selection
  • Linear mixed-effects models
  • Orthogonality

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