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On consistency of Kendall's tau under censoring

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  • David Oakes

Abstract

Necessary and sufficient conditions for consistency of a simple estimator of Kendall's tau under bivariate censoring are presented. The results are extended to data subject to bivariate left truncation as well as right censoring. Copyright 2008, Oxford University Press.

Suggested Citation

  • David Oakes, 2008. "On consistency of Kendall's tau under censoring," Biometrika, Biometrika Trust, vol. 95(4), pages 997-1001.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:4:p:997-1001
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    File URL: http://hdl.handle.net/10.1093/biomet/asn037
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    Cited by:

    1. Austin, Matthew D. & Betensky, Rebecca A., 2014. "Eliminating bias due to censoring in Kendall’s tau estimators for quasi-independence of truncation and failure," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 16-26.
    2. van den Berg, Gerard J. & Effraimidis, Georgios, 2014. "Dependence Measures in Bivariate Gamma Frailty Models," IZA Discussion Papers 8083, Institute of Labor Economics (IZA).
    3. Ruosha Li & Yu Cheng & Qingxia Chen & Jason Fine, 2017. "Quantile association for bivariate survival data," Biometrics, The International Biometric Society, vol. 73(2), pages 506-516, June.
    4. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.

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