Journal Published Online: 18 January 2016
Volume 44, Issue 5

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

CODEN: JTEVAB

Abstract

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.

Author Information

Cai, J.
Dept. of Applied Mathematics, Northwestern Polytechnical Univ., Xi'an, China; and College of Science, Guizhou Minzu Univ., Guiyang, CN
Shi, Y.
Dept. of Applied Mathematics, Northwestern Polytechnical Univ., Xi'an, CN
Liu, B.
Dept. of Applied Mathematics, Northwestern Polytechnical Univ., Xi'an, CN
Pages: 11
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Stock #: JTE20150141
ISSN: 0090-3973
DOI: 10.1520/JTE20150141