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Nonparametric estimation of the conditional survival function with double smoothing

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  • Rebeca Peláez
  • Ricardo Cao
  • Juan M. Vilar

Abstract

In this paper, a conditional survival function estimator for censored data is studied. It is based on a double smoothing technique: both the covariate and the variable of interest (usually, the time) are smoothed. Asymptotic expressions for the bias and the variance and the asymptotic normality of the smoothed survival estimator derived from Beran's estimator are found. A simulation study shows the performance of the smoothed Beran's estimator of the conditional survival function and compares it with the smoothed one only in the covariate. The influence of the two smoothing parameters involved in both estimators is also studied.

Suggested Citation

  • Rebeca Peláez & Ricardo Cao & Juan M. Vilar, 2022. "Nonparametric estimation of the conditional survival function with double smoothing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(4), pages 1063-1090, October.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:4:p:1063-1090
    DOI: 10.1080/10485252.2022.2102631
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    Cited by:

    1. Yikai Liu & Ruozheng Wu & Aimin Yang, 2023. "Research on Medical Problems Based on Mathematical Models," Mathematics, MDPI, vol. 11(13), pages 1-26, June.

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