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Bayesian analysis of discrete time warranty data

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The analysis of warranty claim data, and their use for prediction, has been a topic of active research in recent years. Field data comprising numbers of units returned under guarantee are examined, covering both situations in which the ages of the failed units are known and in which they are not. The latter case poses particular computational problems for likelihood-based methods because of the large number of feasible failure patterns that must be included as contributions to the likelihood function. For prediction of future warranty exposure, which is of central concern to the manufacturer, the Bayesian approach is adopted. For this, Markov chain Monte Carlo methodology is developed.

Keywords: Data augmentation; Discrete–continuous Markov chain Monte Carlo methodology; Guarantee exposure; Marginal counts; Non-repairable failure; Product reliability; Warranty

Document Type: Research Article


Affiliations: Imperial College, London, UK.

Publication date: January 1, 2004


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