Prediction of Future Failures for Heterogeneous Reliability Field Data
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This article introduces methods for constructing prediction bounds or intervals to predict the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often suffers from limited information due to small sample sizes, censoring, and truncation. We use a Bayesian hierarchical model to jointly model multiple lifetime distributions arising from different sub-populations of similar products. By borrowing information across sub-populations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Two applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.
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