Statistical Inference for a Simple Step Stress Model with Competing Risks Based on Generalized Type-I Hybrid Censoring

Mao Song (), Liu Bin () and Shi Yimin ()
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Mao Song: School of Economics and Management, Shanxi University, Taiyuan030006, China
Liu Bin: School of Applied Science, Taiyuan University of Science and Technology, Taiyuan030024, China
Shi Yimin: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an710072, China

Journal of Systems Science and Information, 2021, vol. 9, issue 5, 533-548

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.

Keywords: step-stress accelerated lifetime testing; generalized hybrid censoring scheme; cumulative exposure model; moment generating function (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:9:y:2021:i:5:p:533-548:n:5

DOI: 10.21078/JSSI-2021-533-16

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