Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring |
| |
| Authors: | Jing Cai Yimin Shi Bin Liu |
| |
| Affiliation: | 1. College of Data Science and Engineering, Guizhou Minzu University, Guiyang, Guizhou, China;2. Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, Shaanxi, China |
| |
| Abstract: | This paper considers the statistical analysis of masked data in a series system, where the components are assumed to have Marshall‐Olkin Weibull distribution. Based on type‐I progressive hybrid censored and masked data, we derive the maximum likelihood estimates, approximate confidence intervals, and bootstrap confidence intervals of unknown parameters. As the maximum likelihood estimate does not exist for small sample size, Gibbs sampling is used to obtain the Bayesian estimates and Monte Carlo method is employed to construct the credible intervals based on Jefferys prior with partial information. Numerical simulations are performed to compare the performances of the proposed methods and one data set is analyzed. |
| |
| Keywords: | bootstrap interval Gibbs sampling masked data Marshall‐Olkin Weibull distribution type‐I progressive hybrid censoring |
|
|
