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Inference for high-dimensional linear expectile regression with de-biasing method

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  • Li, Xiang
  • Li, Yu-Ning
  • Zhang, Li-Xin
  • Zhao, Jun

Abstract

The methodology for the inference problem in high-dimensional linear expectile regression is developed. By transforming the expectile loss into a weighted-least-squares form and applying a de-biasing strategy, Wald-type tests for multiple constraints within a regularized framework are established. An estimator for the pseudo-inverse of the generalized Hessian matrix in high dimension is constructed using general amenable regularizers, including Lasso and SCAD, with its consistency demonstrated through a novel proof technique. Simulation studies and real data applications demonstrate the efficacy of the proposed test statistic in both homoscedastic and heteroscedastic scenarios.

Suggested Citation

  • Li, Xiang & Li, Yu-Ning & Zhang, Li-Xin & Zhao, Jun, 2024. "Inference for high-dimensional linear expectile regression with de-biasing method," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:csdana:v:198:y:2024:i:c:s0167947324000811
    DOI: 10.1016/j.csda.2024.107997
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    References listed on IDEAS

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