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• W2098978426 abstract "Fully Bayesian approaches to analysis can be overly ambitious where there exist realistic limitations on the ability of experts to provide prior distributions for all relevant parameters. This research was motivated by situations where expert judgement exists to support the development of prior distributions describing the number of faults potentially inherent within a design but could not support useful descriptions of the rate at which they would be detected during a reliability-growth test. This paper develops inference properties for a reliability-growth model. The approach assumes a prior distribution for the ultimate number of faults that would be exposed if testing were to continue ad infinitum, but estimates the parameters of the intensity function empirically. A fixed-point iteration procedure to obtain the maximum likelihood estimate is investigated for bias and conditions of existence. The main purpose of this model is to support inference in situations where failure data are few. A procedure for providing statistical confidence intervals is investigated and shown to be suitable for small sample sizes. An application of these techniques is illustrated by an example." @default.
• W2098978426 created "2016-06-24" @default.
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• W2098978426 date "2003-06-01" @default.
• W2098978426 modified "2023-09-27" @default.
• W2098978426 title "Confidence intervals for reliability-growth models with small sample-sizes" @default.
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• W2098978426 doi "https://doi.org/10.1109/tr.2003.811865" @default.
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