Hard thresholding regression

Qiang Sun, Bai Jiang, Hongtu Zhu and Joseph G. Ibrahim

Scandinavian Journal of Statistics, 2019, vol. 46, issue 1, 314-328

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.

Date: 2019
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https://doi.org/10.1111/sjos.12353

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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:46:y:2019:i:1:p:314-328

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