期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 49, 期 9, 页码 2402-2418出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2018.1520869
关键词
Bayesian D-optimality; General equivalence theorem; Quantile regression; Accelerated life tests
Quantile regression has emerged as a significant extension of traditional linear models, and its appealing features, such as robustness, efficiency in the presence of censoring and flexibility of modeling stress-life relationship, have recently been recognized for analyzing accelerated life test data. Based on these merits, we present a method for planning accelerated life test in the quantile regression framework for better analysis of the ALT data. Bayesian D-optimality criterion based on accuracy of model parameters on a whole is used to find optimum test plans. We apply the criterion to accelerated life test planning for estimating a distribution quantile, and there is uncertainty as to which model best describes the lifetime distribution. Further, the proposed method is able to handle non-constant scale parameter models. General equivalence theorem is used to verify the global optimality of the numerically optimized ALT plan.
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