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

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  • Zhangsheng Yu
  • Xihong Lin

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. Copyright 2008, Oxford University Press.

Suggested Citation

  • Zhangsheng Yu & Xihong Lin, 2008. "Nonparametric regression using local kernel estimating equations for correlated failure time data," Biometrika, Biometrika Trust, vol. 95(1), pages 123-137.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:1:p:123-137
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    File URL: http://hdl.handle.net/10.1093/biomet/asm081
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    Cited by:

    1. Yassin Mazroui & Audrey Mauguen & Simone Mathoulin-Pélissier & Gaetan MacGrogan & Véronique Brouste & Virginie Rondeau, 2016. "Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 191-215, April.
    2. Jin, Zhezhen & He, Wenqing, 2016. "Local linear regression on correlated survival data," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 285-294.
    3. Yuanjia Wang & Tanya P. Garcia & Yanyuan Ma, 2012. "Nonparametric Estimation for Censored Mixture Data With Application to the Cooperative Huntington’s Observational Research Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1324-1338, December.
    4. Ming Ouyang & Xinyuan Song, 2020. "Bayesian Local Influence of Generalized Failure Time Models with Latent Variables and Multivariate Censored Data," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 298-316, July.

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