Nonparametric regression using local kernel estimating equations for correlated failure time data

Zhangsheng Yu, Xihong Lin

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

We study nonparametric regression for correlated failure time data. Kernel estimating equations are used to estimate nonparametric covariate effects. Independent and weighted-kernel estimating equations are studied. The derivative of the nonparametric function is first estimated and the nonparametric function is then estimated by integrating the derivative estimator. We show that the nonparametric kernel estimator is consistent for any arbitrary working correlation matrix and that its asymptotic variance is minimized by assuming working independence. We evaluate the performance of the proposed kernel estimator using simulation studies, and apply the proposed method to the western Kenya parasitaemia data.

Original languageEnglish (US)
Pages (from-to)123-137
Number of pages15
JournalBiometrika
Volume95
Issue number1
DOIs
StatePublished - Mar 2008
Externally publishedYes

Fingerprint

Correlated Failure Times
Failure Time Data
Correlated Data
Parasitemia
Estimating Equation
Kenya
Nonparametric Regression
Kernel Estimator
kernel
Derivatives
seeds
Derivative
Nonparametric Estimator
Correlation Matrix
Asymptotic Variance
Covariates
parasitemia
Simulation Study
Estimator
Evaluate

Keywords

  • Asymptotics
  • Clustered survival data
  • Marginal model
  • Sandwich Estimator
  • Weighted kernel smoothing
  • Working-independence kernel estimator

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Statistics and Probability
  • Mathematics(all)
  • Applied Mathematics

Cite this

Nonparametric regression using local kernel estimating equations for correlated failure time data. / Yu, Zhangsheng; Lin, Xihong.

In: Biometrika, Vol. 95, No. 1, 03.2008, p. 123-137.

Research output: Contribution to journalArticle

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