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Empirical copulas for consecutive survival data

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  • E. Strzalkowska-Kominiak
  • W. Stute

Abstract

In the analysis of medical data the whole lifetime is often split into pieces characterizing the various stages in the development of a chronical disease. In this paper we provide a nonparametric copula function estimator for two consecutive survival data which are subject to truncation and right censorship. We also discuss an extension of Spearman’s Rho and Kendall’s Tau to the present situation. Copyright Sociedad de Estadística e Investigación Operativa 2013

Suggested Citation

  • E. Strzalkowska-Kominiak & W. Stute, 2013. "Empirical copulas for consecutive survival data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 688-714, November.
    • Handle: RePEc:spr:testjl:v:22:y:2013:i:4:p:688-714
    DOI: 10.1007/s11749-013-0339-1
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    References listed on IDEAS

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    1. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    2. Segers, Johan, 2012. "Asymptotics of empirical copula processes under non-restrictive smoothness assumptions," LIDAM Reprints ISBA 2012009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    Cited by:

    1. T. Emura & K. Murotani, 2015. "An algorithm for estimating survival under a copula-based dependent truncation model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 734-751, December.
    2. Jone Ascorbebeitia & Eva Ferreira & Susan Orbe, 2022. "Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 931-949, December.
    3. Takeshi Emura & Chi-Hung Pan, 2020. "Parametric likelihood inference and goodness-of-fit for dependently left-truncated data, a copula-based approach," Statistical Papers, Springer, vol. 61(1), pages 479-501, February.

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