A nonparametric survival function estimator via censored kernel quantile regressions

Seung Jun Shin, Hao Helen Zhang, Yichao Wu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In survival data analysis, a central interest is to identify the relationship between a possibly censored survival time and explanatory covariates. In this article, a new censored quantile regression method is proposed and studied in the framework of reproducing kernel Hilbert spaces (RKHS). We first establish the joint piecewise linearity of the regression parameters as a function of regularization parameter λ and quantile level τ. An efficient algorithm is then developed to compute the entire two-dimensional solution surface over the (λ × τ )-plane. Finally, a piecewise linear conditional survival function estimator is constructed based on the solution surface. The method provides a new and flexible survival function estimator without requiring such rigid model assumptions as linearity of the survival time or proportionality of the hazards. One important advantage of the estimator is that it can handle moderately high-dimensional covariates. We carry out an asymptotic analysis to justify the proposed method theoretically, and numerical results are shown to illustrate its competitive finite-sample performance under various simulated scenarios and real applications.

Original languageEnglish
Pages (from-to)457-478
Number of pages22
JournalStatistica Sinica
Issue number1
Publication statusPublished - 2017 Jan


  • Censored kernel quantile regression
  • Conditional survival function
  • Solution surface

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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