• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.131-148
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• 2019

Constant stress partially accelerated life tests are studied according to exponentiated Weibull distribution. Grounded on multiple censoring, the maximum likelihood estimators are determined in connection with unknown distribution parameters and accelerated factor. The confidence intervals of the unknown parameters and acceleration factor are constructed for large sample size. However, it is not possible to obtain the Bayes estimates in plain form, so we apply a Markov chain Monte Carlo method to deal with this issue, which permits us to create a credible interval of the associated parameters. Finally, based on constant stress partially accelerated life tests scheme with exponentiated Weibull distribution under multiple censoring, the illustrative example and the simulation results are used to investigate the maximum likelihood, and Bayesian estimates of the unknown parameters.

• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.27-38
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• 1998

Consider a life testing experiment in which multiple two-component shared parallel systems are put on test, and the test is terminated at a specified number of system failures. The bivariate data obtained from such a system-level life testing can be classified into three classes: 1) the case of failed two components with known failure times, 2) the case of censored two components, and 3) the case of one censored component and the other failed component of which the failure time might be known or unknown. Under this censoring scheme and the assumption of Freund's bivariate exponential life distribution, the maximum likelihood estimators are obtained. Results of comparative studies based on Monte Carlo simulation are presented.