Weighted expectile regression with covariates missing at random. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Pan, Yingli; Liu, Zhan; Song, Guangyu
• Is Part Of: Communications in statistics. Volume 52:Issue 3(2023)
• Page Start: 1057
• Page End: 1076
• Publication Date: 2023-03-04
• Subjects: Expectile regression -- MAR -- pADMM algorithm -- weighted estimator; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2021.1873371
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: When covariate information is collected through opinion surveys, experimental science, medical research and so on, incomplete records may result in missing information. Expectile regression is used in many fields; however, many fewer developments have been made with expectile regression for data with missing information. In this paper, we develop a weighted expectile regression approach for estimating the conditional expectile when covariates are missing at random (MAR). The asymptotic normality of the proposed weighted estimators is established. A proximal alternating direction method of multipliers (pADMM) algorithm is developed for the calculation of the weighted estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. We illustrate the application of the method by analyzing a real data set from Beijing Olympic sports center monitoring stations.