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An elastic-net penalized expectile regression with applications

Author

Listed:
  • Q.F. Xu
  • X.H. Ding
  • C.X. Jiang
  • K.M. Yu
  • L. Shi

Abstract

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.

Suggested Citation

  • Q.F. Xu & X.H. Ding & C.X. Jiang & K.M. Yu & L. Shi, 2021. "An elastic-net penalized expectile regression with applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(12), pages 2205-2230, September.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2205-2230
    DOI: 10.1080/02664763.2020.1787355
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    Cited by:

    1. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.

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