Ramamoorthy and F. SE-8 4 , July , pp. Schick and R. SE-4 2 , March , pp. Computer Conf. Shanthikumar and S. Smith and S. Thompson, Jr. Yamada and S. IECE Japan , vol. JD 7 , July , pp. Controls , vol. JD 12 , December , pp. Systems Sci. Yamada, S. Osaki and H. E 2 , Pebruary , pp, 79— Yamada, H.
Narihisa and S. Shigeru Yamada 1 Shunji Osaki 2 1. Personalised recommendations. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate.
A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data. Related Articles:. Home References Article citations. Journals A-Z.
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Skip to main content. Search SpringerLink Search. Abstract This paper presents a generalized logistic software reliability growth model that integrates time-dependent fault detection rate and imperfect removing rate per fault. References 1.
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