Bayesian analysis of competing risks with partially masked cause of failure

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.

[1]  Els Goetghebeur,et al.  Analysis of competing risks survival data when some failure types are missing , 1995 .

[2]  Frank M. Guess,et al.  Estimating system and component reliabilities under partial information on cause of failure , 1991 .

[3]  Chiranjit Mukhopadhyay,et al.  Bayesian analysis for masked system failure data using non-identical Weibull models , 1999 .

[4]  F. Guess,et al.  Exact maximum likelihood estimation using masked system data , 1993 .

[5]  James O. Berger,et al.  Bayesian Analysis for the Poly-Weibull Distribution , 1993 .

[6]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[7]  Frank M. Guess,et al.  Bayesian inference for masked system lifetime data , 1995 .

[8]  William Q. Meeker,et al.  SPLIDA (S-PLUS Life Data Analysis) , 2004 .

[9]  Y. Tong,et al.  Convex Functions, Partial Orderings, and Statistical Applications , 1992 .

[10]  M. Newton,et al.  Easy Estilnation of Nonnalizing Constants and Bayes Factors from Posterior Simulation: Stabilizing the Harmonic :Nlean Estimator 1 , 2000 .

[11]  Frank M. Guess,et al.  Bayes estimation of component-reliability from masked system-life data , 1996, IEEE Trans. Reliab..

[12]  Masami Miyakawa,et al.  Analysis of Incomplete Data in Competing Risks Model , 1984, IEEE Transactions on Reliability.

[13]  Stefan H. Steiner,et al.  Monitoring processes with data censored owing to competing risks by using exponentially weighted moving average control charts , 2001 .

[14]  Sanjib Basu,et al.  Ch. 19. Analysis of masked failure data under competing risks , 2001 .

[15]  W. Q. Meeker,et al.  A failure-time model for infant-mortality and wearout failure modes , 1999 .

[16]  I Guttman,et al.  Dependent masking and system life data analysis: Bayesian inference for two-component systems , 1995, Lifetime data analysis.

[17]  M. Banerjee,et al.  Bayesian Inference for Kappa from Single and Multiple Studies , 2000, Biometrics.

[18]  W. Gilks,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 1992 .

[19]  Asit P. Basu,et al.  Bayesian analysis of incomplete time and cause of failure data , 1997 .

[20]  Frank M. Guess,et al.  System life data analysis with dependent partial knowledge on the exact cause of system failure , 1994 .

[21]  Emmanuel Yashchin,et al.  Ch. 18. Statistical analysis for masked data , 2001 .

[22]  M. Crowder.  Classical Competing Risks , 2001 .

[23]  W. Nelson.  Statistical Methods for Reliability Data , 1998 .