An elastic-net penalized expectile regression with applications. Issue 12 (10th September 2021)

• Record Type: Journal Article
• Authors: Xu, Q.F.; Ding, X.H.; Jiang, C.X.; Yu, K.M.; Shi, L.
• Is Part Of: Journal of applied statistics. Volume 48:Issue 12(2021)
• Page Start: 2205
• Page End: 2230
• Publication Date: 2021-09-10
• Subjects: Expectile regression -- elastic-net -- SNCD -- variable selection -- high-dimensional data; 62J05; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2020.1787355
• Languages: English
• ISSNs: 0266-4763

Abstract

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.