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Hard thresholding regression

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Listed:
  • Qiang Sun
  • Bai Jiang
  • Hongtu Zhu
  • Joseph G. Ibrahim

Abstract

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ℓ0‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.

Suggested Citation

  • Qiang Sun & Bai Jiang & Hongtu Zhu & Joseph G. Ibrahim, 2019. "Hard thresholding regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(1), pages 314-328, March.
    • Handle: RePEc:bla:scjsta:v:46:y:2019:i:1:p:314-328
    DOI: 10.1111/sjos.12353
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