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Bayesian reliability modeling for masked system lifetime data

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  • Kuo, Lynn
  • Yang, Tae Young

Abstract

In the masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components. In this paper we consider various probability models for the conditional masking probabilities that identify the set of possible failed components given the true cause of failure and the system's failure time. In addition to exponential distributions for the component lifetimes, we consider Weibull distributions. A Bayesian approach that uses Gibbs sampling will be developed for each of the models. Model selection by a predictive approach will also be developed. We show that improved inference can be obtained by modeling the masking probabilities.

Suggested Citation

  • Kuo, Lynn & Yang, Tae Young, 2000. "Bayesian reliability modeling for masked system lifetime data," Statistics & Probability Letters, Elsevier, vol. 47(3), pages 229-241, April.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:3:p:229-241
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    References listed on IDEAS

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    1. B. Reiser & I. Guttman & Dennis K. J. Lin & Frank M. Guess & John S. Usher, 1995. "Bayesian Inference for Masked System Lifetime Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(1), pages 79-90, March.
    2. Gastaldi, Tommaso, 1994. "Improved maximum likelihood estimation for component reliabilities with Miyakawa--Usher--Hodgson--Guess' estimators under censored search for the cause of failure," Statistics & Probability Letters, Elsevier, vol. 19(1), pages 5-18, January.
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    Cited by:

    1. Mazucheli, Josmar & Louzada-Neto, Francisco & Achcar, Jorge A., 2001. "Bayesian inference for polyhazard models in the presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 1-14, November.
    2. Yu, Qiqing & Qin, Hao & Wang, Jiaping, 2010. "About conditional masking probability models," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1174-1179, August.
    3. Qiqing Yu & G. Wong & Hao Qin & Jiaping Wang, 2012. "Random partition masking model for censored and masked competing risks data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 69-85, February.
    4. Juliana Fachini & Edwin Ortega & Francisco Louzada-Neto, 2008. "Influence diagnostics for polyhazard models in the presence of covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 413-433, October.
    5. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
    6. Yosra Yousif & Faiz Elfaki & Meftah Hrairi & Oyelola Adegboye, 2022. "Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model," Mathematics, MDPI, vol. 10(17), pages 1-10, August.
    7. 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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