A simultaneous variable selection methodology for linear mixed models

Juming Pan, Junfeng Shang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Selecting an appropriate structure for a linear mixed model serves as an appealing problem in a number of applications such as in the modelling of longitudinal or clustered data. In this paper, we propose a variable selection procedure for simultaneously selecting and estimating the fixed and random effects. More specifically, a profile log-likelihood function, along with an adaptive penalty, is utilized for sparse selection. The Newton-Raphson optimization algorithm is performed to complete the parameter estimation. By jointly selecting the fixed and random effects, the proposed approach increases selection accuracy compared with two-stage procedures, and the usage of the profile log-likelihood can improve computational efficiency in one-stage procedures. We prove that the proposed procedure enjoys the model selection consistency. A simulation study and a real data application are conducted for demonstrating the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)3323-3337
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number17
StatePublished - Nov 22 2018


  • Linear mixed models
  • penalized variable selection
  • profile log-likelihood

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