Single index quantile regression for censored data

  • Eliana ChristouEmail author
  • Michael G. Akritas
Original Paper


Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic. It is particularly relevant for the analysis of censored survival data as an alternative to proportional hazards and the accelerated failure time models. Such data occur frequently in biostatistics, environmental sciences, social sciences and econometrics. There is a large body of work for linear/nonlinear QR models for censored data, but it is only recently that the single index quantile regression (SIQR) model has received some attention. However, the only existing method for fitting the SIQR model for censored data uses an iterative algorithm and no asymptotic theory for the resulting estimator of the parametric component is given. We propose a non-iterative estimation algorithm and derive the asymptotic distribution of the proposed estimator under heteroscedasticity. Results from simulation studies evaluating the finite sample performance of the proposed estimator are reported.


Censored data Dimension reduction Index model Quantile regression 



The authors wish to thank the two referees, whose comments lead to improvements in the presentation of this paper.

Supplementary material

10260_2019_450_MOESM1_ESM.pdf (232 kb)
Supplementary material 1 (pdf 231 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of North Carolina at CharlotteCharlotteUSA
  2. 2.Department of StatisticsThe Pennsylvania State UniversityState CollegeUSA

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