Median regression using nonparametric kernel estimation

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Abstract

The fitting of heteroscedastic median regression models to right censored data has been a topic of much research in survival analysis in recent years. McKeague et al. (2001) used the missing information principle to propose an estimator for the regression parameters, and derived the asymptotic properties of their estimator assuming that the covariate takes values in a finite set. In this paper the large sample properties of their estimator are derived when the covariate is continuous. A kernel conditional Kaplan-Meier estimator is used in the missing information principle estimating function. A simulation study involving a one-dimensional covariate is presented.

Original languageEnglish (US)
Pages (from-to)583-605
Number of pages23
JournalJournal of Nonparametric Statistics
Volume14
Issue number5
DOIs
StatePublished - Oct 1 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Asymptotic normality
  • Bandwidth sequence
  • Least absolute deviation
  • Local linearity
  • U-statistic
  • Uniform consistency

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