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A semi-parametric approach to feature selection in high-dimensional linear regression models

Author

Listed:
  • Yuyang Liu

    (Shanghai Jiao Tong University)

  • Pengfei Pi

    (Shanghai Jiao Tong University)

  • Shan Luo

    (Shanghai Jiao Tong University)

Abstract

We propose a novel semi-parametric approach to feature selection in high-dimensional linear regression models. This sequential procedure is robust to the unknown error distribution including heavy-tailed distributions. At each step of this procedure, we add the feature with the largest absolute value of the estimated partial profile score into the model. The procedure terminates when a model selection criterion is met. Theoretically, we establish this procedure’s selection consistency under regular conditions. Computationally, extensive numerical studies together with a real data application are provided to demonstrate its advantage over existing representative methods in terms of selection accuracy and computation cost.

Suggested Citation

  • Yuyang Liu & Pengfei Pi & Shan Luo, 2023. "A semi-parametric approach to feature selection in high-dimensional linear regression models," Computational Statistics, Springer, vol. 38(2), pages 979-1000, June.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01254-z
    DOI: 10.1007/s00180-022-01254-z
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    References listed on IDEAS

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