Mixture Failure Rate: A Study Based on Cross-entropy and MCMC Method

被引：0

作者：

Tien Thanh Thach ^{[1
]}

Bris, Radim ^{[1
]}

Coolen, Frank P. A. ^{[2
]}

机构：

[1] VSB Tech Univ Ostrava, Dept Appl Math, Ostrava, Czech Republic

[2] Univ Durham, Dept Math Sci, Durham, England

来源：

2017 INTERNATIONAL CONFERENCE ON INFORMATION AND DIGITAL TECHNOLOGIES (IDT) | 2017年

关键词：

Failure time distribution; failure rate; Markov chain Monte Carlo; Metropolis-Hastings algorithm; maximum likelihood estimation; Cross-Entropy algorithm; LIFE DISTRIBUTIONS; RELIABILITY; PARAMETERS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the parameters and reliability characteristics of the mixture of the failure time distribution are estimated based on a complete sample using both Markov chain Monte Carlo (MCMC) method and maximum likelihood estimation via cross-entropy (CE) algorithm. While maximum likelihood estimation is the most frequently used method for parameter estimation, MCMC has recently emerged as a good alternative. The most popular MCMC method, called the Metropolis-Hastings algorithm, is used to provide a complete analysis of the concerned posterior distribution. A simulation study is provided to compare MCMC with CE, and differences between the estimates obtained by the two approaches are evaluated.

引用

页码：373 / 382

页数：10

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