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Nonparametric predictive inference for combined competing risks data

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  • Coolen-Maturi, Tahani
  • Coolen, Frank P.A.

Abstract

The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies.

Suggested Citation

  • Coolen-Maturi, Tahani & Coolen, Frank P.A., 2014. "Nonparametric predictive inference for combined competing risks data," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 87-97.
    • Handle: RePEc:eee:reensy:v:126:y:2014:i:c:p:87-97
    DOI: 10.1016/j.ress.2014.01.007
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    References listed on IDEAS

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    1. Coolen-Maturi, Tahani, 2014. "Nonparametric predictive pairwise comparison with competing risks," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 146-153.
    2. T A Maturi & P Coolen-Schrijner & F P A Coolen, 2010. "Nonparametric predictive inference for competing risks," Journal of Risk and Reliability, , vol. 224(1), pages 11-26, March.
    3. Bunea, Cornel & Mazzuchi, Thomas A. & Sarkani, Shahram & Chang, Hai-Chin, 2008. "Application of modern reliability database techniques to military system data," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 14-27.
    4. Jiang, R., 2010. "Discrete competing risk model with application to modeling bus-motor failure data," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 981-988.
    5. Sarhan, Ammar M. & Hamilton, David C. & Smith, B., 2010. "Statistical analysis of competing risks models," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 953-962.
    6. T Coolen-Maturi & F P A Coolen, 2011. "Unobserved, re-defined, unknown or removed failure modes in competing risks," Journal of Risk and Reliability, , vol. 225(4), pages 461-474, December.
    7. Bocchetti, D. & Giorgio, M. & Guida, M. & Pulcini, G., 2009. "A competing risk model for the reliability of cylinder liners in marine Diesel engines," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1299-1307.
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    Cited by:

    1. Bi Liu & Yunlong Teng & Qi Huang, 2020. "RETRACTED: A novel imprecise reliability prediction method for incomplete lifetime data based on two-parameter Weibull distribution," Journal of Risk and Reliability, , vol. 234(1), pages 208-218, February.
    2. Coolen-Maturi, Tahani, 2014. "Nonparametric predictive pairwise comparison with competing risks," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 146-153.

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