A New Approach to Censored Quantile Regression Estimation. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Yang, Xiaorong; Narisetty, Naveen Naidu; He, Xuming
• Is Part Of: Journal of computational and graphical statistics. Volume 27:Issue 2(2018)
• Page Start: 417
• Page End: 425
• Publication Date: 2018-04-03
• Subjects: Censored response -- Conditional quantile -- Data augmentation -- Imputation; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2017.1385469
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Quantile regression provides an attractive tool to the analysis of censored responses, because the conditional quantile functions are often of direct interest in regression analysis, and moreover, the quantiles are often identifiable while the conditional mean functions are not. Existing methods of estimation for censored quantiles are mostly limited to singly left- or right-censored data, with some attempts made to extend the methods to doubly censored data. In this article, we propose a new and unified approach, based on a variation of the data augmentation algorithm, to censored quantile regression estimation. The proposed method adapts easily to different forms of censoring including doubly censored and interval censored data, and somewhat surprisingly, the resulting estimates improve on the performance of the best known estimators with singly censored data. Supplementary material for this article is available online.