Open Access
Translator Disclaimer
2020 Efficient estimation in expectile regression using envelope models
Tuo Chen, Zhihua Su, Yi Yang, Shanshan Ding
Electron. J. Statist. 14(1): 143-173 (2020). DOI: 10.1214/19-EJS1664


As a generalization of the classical linear regression, expectile regression (ER) explores the relationship between the conditional expectile of a response variable and a set of predictor variables. ER with respect to different expectile levels can provide a comprehensive picture of the conditional distribution of the response variable given the predictors. We adopt an efficient estimation method called the envelope model ([8]) in ER, and construct a novel envelope expectile regression (EER) model. Estimation of the EER parameters can be performed using the generalized method of moments (GMM). We establish the consistency and derive the asymptotic distribution of the EER estimators. In addition, we show that the EER estimators are asymptotically more efficient than the ER estimators. Numerical experiments and real data examples are provided to demonstrate the efficiency gains attained by EER compared to ER, and the efficiency gains can further lead to improvements in prediction.


Download Citation

Tuo Chen. Zhihua Su. Yi Yang. Shanshan Ding. "Efficient estimation in expectile regression using envelope models." Electron. J. Statist. 14 (1) 143 - 173, 2020.


Received: 1 April 2018; Published: 2020
First available in Project Euclid: 7 January 2020

zbMATH: 07154985
MathSciNet: MR4047997
Digital Object Identifier: 10.1214/19-EJS1664

Keywords: envelope model , expectile regression , generalized method of moments , sufficient dimension reduction


Vol.14 • No. 1 • 2020
Back to Top