Weighted quantile average estimation for general linear models with missing covariates. Issue 17 (2nd September 2017)

• Record Type: Journal Article
• Authors: Sun, Jing
• Is Part Of: Communications in statistics. Volume 46:Issue 17(2017)
• Page Start: 8706
• Page End: 8722
• Publication Date: 2017-09-02
• Subjects: Inverse probability weighting -- missing mechanism -- optimal weights -- weighted quantile average estimator.; 62J12 -- 62F12 -- 62G20 -- 62G35; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2016.1189570
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: We develop a weighted quantile average estimation technique for general linear models with missing covariates. The proposed method is based on optimally combining information over different quantiles via multiple quantile regressions. We establish asymptotic normality of the weighted quantile average estimators when selection probabilities are known, estimated non parametrically and estimated parametrically, respectively. Moreover, we compute optimal weights by minimizing asymptotic variance and then obtain the corresponding optimal weighted quantile average estimates, whose asymptotic variance approaches the Cram r–Rao lower bound under appropriate conditions. Numerical studies and a real data analysis are conducted to investigate the finite sample performance of the proposed method.