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On Testing Dependence between Time to Failure and Cause of Failure via Conditional Probabilities

Author

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  • Isha Dewan
  • J. V. Deshpande
  • S. B. Kulathinal

Abstract

. Dependence structures between the failure time and the cause of failure are expressed in terms of the monotonicity properties of the conditional probabilities involving the cause of failure and the failure time. These properties of the conditional probabilities are used for testing four types of departures from the independence of the failure time and the cause of failure and tests based on U‐statistics are proposed. In the process, a concept of concordance and discordance between a continuous and a binary variable is introduced to propose a statistical test. The proposed tests are applied to two illustrative applications.

Suggested Citation

  • Isha Dewan & J. V. Deshpande & S. B. Kulathinal, 2004. "On Testing Dependence between Time to Failure and Cause of Failure via Conditional Probabilities," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 79-91, March.
    • Handle: RePEc:bla:scjsta:v:31:y:2004:i:1:p:79-91
    DOI: 10.1111/j.1467-9469.2004.00374.x
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    Cited by:

    1. Somnath Datta & Dipankar Bandyopadhyay & Glen A. Satten, 2010. "Inverse Probability of Censoring Weighted U‐statistics for Right‐Censored Data with an Application to Testing Hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 680-700, December.
    2. Isha Dewan & Sangita Kulathinal, 2007. "On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing," PLOS ONE, Public Library of Science, vol. 2(12), pages 1-7, December.
    3. Bandyopadhyay, Dipankar & Jácome, M. Amalia, 2016. "Comparing conditional survival functions with missing population marks in a competing risks model," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 150-160.

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