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Nonparametric estimation and test of conditional Kendall's tau under semi-competing risks data and truncated data

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  • Jin-Jian Hsieh
  • Wei-Cheng Huang

Abstract

In this article, we focus on estimation and test of conditional Kendall's tau under semi-competing risks data and truncated data. We apply the inverse probability censoring weighted technique to construct an estimator of conditional Kendall's tau, . Then, this study provides a test statistic for , where . When two random variables are quasi-independent, it implies . Thus, is a proxy for quasi-independence. Tsai [12], and Martin and Betensky [10] considered the testing problem for quasi-independence. Via simulation studies, we compare the three test statistics for quasi-independence, and examine the finite-sample performance of the proposed estimator and the suggested test statistic. Furthermore, we provide the large sample properties for our proposed estimator. Finally, we provide two real data examples for illustration.

Suggested Citation

  • Jin-Jian Hsieh & Wei-Cheng Huang, 2015. "Nonparametric estimation and test of conditional Kendall's tau under semi-competing risks data and truncated data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1602-1616, July.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1602-1616
    DOI: 10.1080/02664763.2015.1004624
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    Cited by:

    1. Derumigny Alexis & Fermanian Jean-David, 2019. "On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior," Dependence Modeling, De Gruyter, vol. 7(1), pages 292-321, January.

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