A New Approach to Censored Quantile Regression Estimation

作者：Yang, Xiaorong; Narisetty, Naveen Naidu^*; He, Xuming

来源：Journal of Computational and Graphical Statistics, 2018, 27(2): 417-425.

DOI：10.1080/10618600.2017.1385469

• Censored response
• Conditional quantile
• Data augmentation
• Imputation

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.

• 出版日期2018
• 单位浙江工商大学