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Adaptive LASSO for linear mixed model selection via profile log-likelihood

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  • Juming Pan
  • Junfeng Shang

Abstract

Mixed model selection is quite important in statistical literature. To assist the mixed model selection, we employ the adaptive LASSO penalized term to propose a two-stage selection procedure for the purpose of choosing both the random and fixed effects. In the first stage, we utilize the penalized restricted profile log-likelihood to choose the random effects; in the second stage, after the random effects are determined, we apply the penalized profile log-likelihood to select the fixed effects. In each stage, the Newton–Raphson algorithm is performed to complete the parameter estimation. We prove that the proposed procedure is consistent and possesses the oracle properties. The simulations and a real data application are conducted for demonstrating the effectiveness of the proposed selection procedure.

Suggested Citation

  • Juming Pan & Junfeng Shang, 2018. "Adaptive LASSO for linear mixed model selection via profile log-likelihood," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(8), pages 1882-1900, April.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:8:p:1882-1900
    DOI: 10.1080/03610926.2017.1332219
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    Cited by:

    1. Qi Zhang, 2022. "High-Dimensional Mediation Analysis with Applications to Causal Gene Identification," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 432-451, December.

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