Abstract
Abstract This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-I hybrid censoring scheme is employed to improve the efficiency and controllability of the test. Firstly, the MLEs for parameters are established based on the cumulative exposure model (CEM). Then the conditional moment generating function (MGF) for unknown parameters is set up using conditional expectation and multiple integral techniques. Thirdly, confidence intervals (CIs) are constructed by the exact MGF-based method, the approximate normality-based method, and the bias-corrected and accelerated (BCa) percentile bootstrap method. Finally, we present simulation studies and an illustrative example to compare the performances of different methods.
Highlights
Accelerated lifetime test (ALT) is often implemented to collect failure data to shorten the testing time
Extensive researches on stress ALT (SSALT) focused on inferential problem or experimental design under traditional censored scheme, but few have considered it in the context of the generalized hybrid censoring scheme
Adopting the coverage probability (CP) as an effective measurement to illustrate the above methods, we present numerical results based on the nominal level of 90%, 95%, 99% in Tables 1, 2
Summary
Accelerated lifetime test (ALT) is often implemented to collect failure data to shorten the testing time. Han and Balakrishnan[4] analyzed a competing risk model in an SSALT based on the CEM model They employed the moment generating function, the normal approximation, and the Bootstrap method to construct confidence intervals. Shafay[18] developed an SSALT under generalized hybrid censoring from the perspective of likelihood as well as Bayes He derived point estimators, confidence intervals, and the optimal stress-changing point. Extensive researches on SSALT focused on inferential problem or experimental design under traditional censored scheme, but few have considered it in the context of the generalized hybrid censoring scheme. Concerning that the generalized hybrid censoring scheme can improve the efficiency and the controllability of experiment, we have discussed inferential problems for a simple SSALT with exponential competing risks data under generalized type-I hybrid censoring scheme. Statistical Inference for a Simple Step Stress Model with Competing Risks
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