التفاصيل البيبلوغرافية
| العنوان: |
An elastic-net penalized expectile regression with applications. |
| المؤلفون: |
Xu, Q.F.1,2 (AUTHOR), Ding, X.H.1 (AUTHOR), Jiang, C.X.1 (AUTHOR) jiangcuixia@hfut.edu.cn, Yu, K.M.3 (AUTHOR), Shi, L.4 (AUTHOR) |
| المصدر: |
Journal of Applied Statistics. Sep2021, Vol. 48 Issue 12, p2205-2230. 26p. 12 Charts, 6 Graphs. |
| مصطلحات موضوعية: |
Monte Carlo method, Distribution (Probability theory), Least squares, Petri nets, Algorithms, Quantile regression, Tacrolimus |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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