A New Approach to Censored Quantile Regression Estimation

Xiaorong Yang, Naveen Naidu Narisetty, Xuming He

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)417-425
Number of pages9
JournalJournal of Computational and Graphical Statistics
Issue number2
StatePublished - Apr 3 2018


  • Censored response
  • Conditional quantile
  • Data augmentation
  • Imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty


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