Analysis of Masked Data in a Series System Subjected to Sources of Shocks Under Type I Progressive Hybrid Censoring

作者：Cai, J.^*; Shi, Y.; Liu, B.

来源：Journal of Testing and Evaluation, 2016, 44(5): 1949-1959.

DOI：10.1520/JTE20150141

• masked data
• fatal shock model
• type I progressive hybrid censoring
• approximate maximum likelihood
• Gibbs sampling

In this paper, we consider the statistical analysis of masked data based on the fatal shock model under type I progressive hybrid censoring. It is assumed that the time of shock follows Weibull distribution. Maximum- likelihood estimators for the unknown parameters are obtained by solving a one- dimensional optimization problem. Approximate maximum-likelihood estimators have been proposed based on a Taylor series expansion, and they have explicit expressions. In addition, the Bayesian approach, combined with Gibbs sampling, are developed based on the assumption that the shape parameter has a log-concave function, and for the given shape parameter, the scale parameters have Gamma-Dirichlet priors. Finally, Monte Carlo simulations are performed to compare the performances of the proposed methods under different progressive censoring schemes and masking levels.

• 出版日期2016-9
• 单位西北工业大学