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Abstract:
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A number of competing accelerated life testing models have been studied for modeling accelerated failure times by many authors (see, for example, Nelson, 1981; Bhattachayya & Fries, 1982b; and Babu and Chaubey, 1996). This articles reviews briefly several aspects and properties for some competing accelerated life testing models for modeling failure times data, namely the log-normal, the log-normal with Box-Cox, the inverse Gaussian, the gamma, and the Weibull models. The maximum likelihood methods are outlined for the estimation of the parameters of these models. Comparison studies are considered, through simulations as well as real-life datasets, based on a parametric bootstrap approach of model evaluation.
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