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Random Subspace Method for high-dimensional regression with the R package regRSM

Author

Listed:
  • Paweł Teisseyre

    (Polish Academy of Sciences)

  • Robert A. Kłopotek

    (Polish Academy of Sciences)

  • Jan Mielniczuk

    (Polish Academy of Sciences
    Warsaw University of Technology)

Abstract

Model selection and variable importance assessment in high-dimensional regression are among the most important tasks in contemporary applied statistics. In our procedure, implemented in the package regRSM, the Random Subspace Method (RSM) is used to construct a variable importance measure. The variables are ordered with respect to the measures computed in the first step using the RSM and then, from the hierarchical list of models given by the ordering, the final subset of variables is chosen using information criteria or validation set. Modifications of the original method such as the weighted Random Subspace Method and the version with initial screening of redundant variables are discussed. We developed parallel implementations which enable to reduce the computation time significantly. In this paper, we give a brief overview of the methodology, demonstrate the package’s functionality and present a comparative study of the proposed algorithm and the competitive methods like lasso or CAR scores. In the performance tests the computational times for parallel implementations are compared.

Suggested Citation

  • Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    • Handle: RePEc:spr:compst:v:31:y:2016:i:3:d:10.1007_s00180-016-0658-2
    DOI: 10.1007/s00180-016-0658-2
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    References listed on IDEAS

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    Cited by:

    1. Łukasz Smaga & Hidetoshi Matsui, 2018. "A note on variable selection in functional regression via random subspace method," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 455-477, August.
    2. Tomasz Górecki & Łukasz Smaga, 2019. "fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data," Computational Statistics, Springer, vol. 34(2), pages 571-597, June.

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