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Bayesian analysis of competing risks with partially masked cause of failure

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  • Sanjib Basu
  • Ananda Sen
  • Mousumi Banerjee

Abstract

Summary. Bayesian analysis of system failure data from engineering applications under a competing risks framework is considered when the cause of failure may not have been exactly identified but has only been narrowed down to a subset of all potential risks. In statistical literature, such data are termed masked failure data. In addition to masking, failure times could be right censored owing to the removal of prototypes at a prespecified time or could be interval censored in the case of periodically acquired readings. In this setting, a general Bayesian formulation is investigated that includes most commonly used parametric lifetime distributions and that is sufficiently flexible to handle complex forms of censoring. The methodology is illustrated in two engineering applications with a special focus on model comparison issues.

Suggested Citation

  • Sanjib Basu & Ananda Sen & Mousumi Banerjee, 2003. "Bayesian analysis of competing risks with partially masked cause of failure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 77-93, January.
    • Handle: RePEc:bla:jorssc:v:52:y:2003:i:1:p:77-93
    DOI: 10.1111/1467-9876.00390
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    Cited by:

    1. Sanjib Basu & Ram C. Tiwari, 2010. "Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 307-329, April.
    2. Basu, Sanjib & Ebrahimi, Nader, 2008. "A note on a transformation under censoring with application to partial least squares regression," Statistics & Probability Letters, Elsevier, vol. 78(10), pages 1161-1164, August.
    3. Daniel Nevo & Reiko Nishihara & Shuji Ogino & Molin Wang, 2018. "The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 425-442, July.
    4. Jiahui Li & Qiqing Yu, 2016. "A consistent NPMLE of the joint distribution function with competing risks data under the dependent masking and right-censoring model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 63-99, January.
    5. Himanshu Rai & Sanjeev K. Tomer & Anoop Chaturvedi, 2021. "Robust estimation with variational Bayes in presence of competing risks," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 207-223, August.

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