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Variable selection for varying-coefficient models with the sparse regularization

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  • Hidetoshi Matsui
  • Toshihiro Misumi

Abstract

Varying-coefficient models are useful tools for analyzing longitudinal data. They can effectively describe a relationship between predictors and responses which are repeatedly measured. We consider the problem of selecting variables in the varying-coefficient models via adaptive elastic net regularization. Coefficients given as functions are expressed by basis expansions, and then parameters involved in the model are estimated by the penalized likelihood method using the coordinate descent algorithm which is derived for solving the problem of sparse regularization. We examine the effectiveness of our modeling procedure through Monte Carlo simulations and real data analysis. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Hidetoshi Matsui & Toshihiro Misumi, 2015. "Variable selection for varying-coefficient models with the sparse regularization," Computational Statistics, Springer, vol. 30(1), pages 43-55, March.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:1:p:43-55
    DOI: 10.1007/s00180-014-0520-3
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    References listed on IDEAS

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    1. Wang, Lifeng & Li, Hongzhe & Huang, Jianhua Z., 2008. "Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1556-1569.
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