Abstract
This article deals with the case of the failure-censored constant-stress partially accelerated life test (CSPALT) for highly reliable materials or products assuming the Pareto distribution of the second kind. The maximum likelihood (ML) method is used to estimate the parameters of the CSPALT model. The performance of ML estimators is investigated via their mean square error. Also, the average confidence interval length (IL) and the associated coverage probability (CP) are obtained. Moreover, optimum CSPALT plans that determine the optimal proportion of the test units allocated to each stress are developed. Such optimum test plans minimize the generalized asymptotic variance (GAV) of the ML estimators of the model parameters. For illustration, Monte Carlo simulation studies are given and a real life example is provided.
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