Double Penalized H-Likelihood for Selection of Fixed and Random Effects in Mixed Effects Models

Peirong Xu, Tao Wang, Hongtu Zhu, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The goal of this paper is to develop a double penalized hierarchical likelihood (DPHL) with a modified Cholesky decomposition for simultaneously selecting fixed and random effects in mixed effects models. DPHL avoids the use of data likelihood, which usually involves a high-dimensional integral, to define an objective function for variable selection. The resulting DPHL-based estimator enjoys the oracle properties with no requirement on the convexity of loss function. Moreover, a two-stage algorithm is proposed to effectively implement this approach. An H-likelihood-based Bayesian information criterion (BIC) is developed for tuning parameter selection. Simulated data and a real data set are examined to illustrate the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)108-128
Number of pages21
JournalStatistics in Biosciences
Volume7
Issue number1
DOIs
Publication statusPublished - 1 May 2015

Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

User-Defined Keywords

  • Hierarchical likelihood
  • Mixed effects models
  • Modified Cholesky decomposition
  • Penalized likelihood
  • Variable selection

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