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Robust Variable Selection in Linear Mixed Models

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  • Yali Fan
  • Guoyou Qin
  • Zhong Yi Zhu

Abstract

In this article, we develop a robust variable selection procedure jointly for fixed and random effects in linear mixed models for longitudinal data. We propose a penalized robust estimator for both the regression coefficients and the variance of random effects based on a re-parametrization of the linear mixed models. Under some regularity conditions, we show the oracle properties of the proposed robust variable selection method. Simulation study shows the robustness of the proposed method against outliers. In the end, the proposed methods is illustrated in the analysis of a real data set.

Suggested Citation

  • Yali Fan & Guoyou Qin & Zhong Yi Zhu, 2014. "Robust Variable Selection in Linear Mixed Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(21), pages 4566-4581, November.
  • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:21:p:4566-4581
    DOI: 10.1080/03610926.2012.724509
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    Cited by:

    1. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.

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