Survival-rate regression using kernel conditional Kaplan-Meier estimators

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Abstract

We consider a regression model in which it is assumed that the conditional survival distribution of the response given the covariate, after transformation using a link function, satisfies a linear regression model. By proper choice of the link function the logistic and Cox models can be obtained. The response is allowed to be subject to right censoring. We consider an estimation procedure for the regression parameters and establish the asymptotic normality of the estimator when the covariate is one-dimensional. The finite sample performance of the proposed estimator is studied through simulations.

Original languageEnglish (US)
Pages (from-to)187-205
Number of pages19
JournalJournal of Statistical Planning and Inference
Volume123
Issue number1
DOIs
StatePublished - Jun 1 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Asymptotically normal
  • Bandwidth
  • Bias
  • Mean squared error
  • Missing information principle
  • U-statistic

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