Inverse censoring weighted median regression

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7 Scopus citations


We implement semiparametric random censorship model aided inference for censored median regression models. This is based on the idea that, when the censoring is specified by a common distribution, a semiparametric survival function estimator acts as an improved weight in the so-called inverse censoring weighted estimating function. We show that the proposed method will always produce estimates of the model parameters that are as good as or better than an existing estimator based on the traditional Kaplan-Meier weights. We also provide an illustration of the method through an analysis of a lung cancer data set.

Original languageEnglish (US)
Pages (from-to)594-603
Number of pages10
JournalStatistical Methodology
Issue number6
StatePublished - Nov 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


  • Asymptotically normal
  • Least absolute deviation
  • Local linearity
  • Loewner ordering
  • Lung cancer study
  • Minimum dispersion statistic


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