Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Volume 366, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cam.2019.112398
Keywords
Accelerated dependent competing risks model; Marshall-Olkin bivariate Weibull distribution; Bias-corrected percentile bootstrap method; Importance sampling method; HPD credible intervals
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Funding
- National Natural Science Foundation of China [71571144, 71401134, 11701406]
- China Scholarship Council [201806290048]
- Humanities and Social Science Fund in Ministry of Education in China [18YJC910009]
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In this paper, we discuss the statistical inference of constant-stress accelerated dependent competing risks model under Type-II hybrid censoring schemes. The dependency structure is modeled by a Marshall-Olkin bivariate Weibull distribution. Both the shape and the scale parameters in the model are assumed to be dependent on the stress levels through a log-linear relationship. The maximum likelihood estimates (MLEs) of the model parameters are derived. Confidence intervals (Cis) of the model parameters are constructed based on the asymptotic normality of MLEs and the bias-corrected percentile bootstrap method. Bayes estimates with the squared error loss function and the highest posterior density (HPD) credible intervals (CIs) are obtained by using an importance sampling method. In addition, the estimates for the accelerated coefficients and the reliability under normal use stress level at mission time are derived. A Monte Carlo simulation study is used to evaluate the performance of the proposed statistical inference methods. A real data example is used to illustrate the methodologies proposed in this paper and to compare the proposed model with the copula model. (C) 2019 Elsevier B.V. All rights reserved.
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