Quantile regression with censored data using generalized L1minimization

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32 Citations (SciVal)


We propose a way to estimate a parametric quantile function when the dependent variable, e.g. the survival time, is censored. We discuss one way to do this, transforming the problem of finding the p-quantile for the true, uncensored, survival times into a problem of finding the q-quantile for the observed, censored, times. The q-value involves the distribution of the censoring times, which is unknown. The estimation of the quantile function is done using the asymmetric L1 technique with weights involving local Kaplan-Meier estimates of the distribution of the censoring limit.
Original languageEnglish
Pages (from-to)509-524
JournalComputational Statistics & Data Analysis
Issue number4
Publication statusPublished - 1997

Subject classification (UKÄ)

  • Probability Theory and Statistics


  • Quantile regression
  • L1 minimization
  • Right censoring
  • Kaplan-Meier estimator


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