Statistical inference for a hybrid system model with incomplete observed data under adaptive progressive hybrid censoring. (24th February 2020)

• Record Type: Journal Article
• Authors: Shi, Xiaolin; Zhan, Pei; Shi, Yimin
• Is Part Of: Concurrency and computation. Volume 32:Number 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-24
• Subjects: adaptive type‐II progressive hybrid censoring -- Bayesian estimate -- credible intervals -- five‐component hybrid systems -- incomplete observed data -- maximum likelihood estimate; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5708
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In this article, the statistical inference of a hybrid system with incomplete observed data is studied based on the adaptive type‐II progressive hybrid censored samples. It is assumed that the lifetime of the component in hybrid systems follows an identical Weibull distribution. The maximum likelihood estimates of the unknown parameters and reliability function are obtained by using the fixed‐point iteration method. Under general entropy loss function, the Bayesian estimates and the highest posterior density credible intervals of the unknown parameters and reliability function are derived using Markov Chain Monte Carlo method. In addition, the approximate confidence intervals and Bootstrap confidence intervals are constructed. Finally, Monte Carlo simulation study is carried out to illustrate the performances of two different point estimates and different confidence intervals.