A Stochastic EM Algorithm for Progressively Censored Data Analysis

被引：20

作者：

Zhang, Mimi ^{[1
]}

Ye, Zhisheng ^{[2
]}

Xie, Min ^{[1
,3
]}

机构：

[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China

[2] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China

[3] Natl Univ Singapore, Dept Ind & Syst Engn, Singapore 117548, Singapore

来源：

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL | 2014年 / 30卷 / 05期

关键词：

stochastic EM algorithm; progressively censored data; lifetime distribution; GAUSSIAN DISTRIBUTION; MIXTURES;

D O I：

10.1002/qre.1522

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet easy-to-implement framework for analyzing progressively censored data by making use of the stochastic EM algorithm. On the basis of this framework, we develop specific stochastic EM procedures for several popular lifetime models. These procedures are shown to be very simple. We then demonstrate the applicability and efficiency of the stochastic EM algorithm by a fatigue life data set with proper modification and by a progressively censored data set from a life test on hard disk drives. Copyright (c) 2013 John Wiley & Sons, Ltd.

引用

页码：711 / 722

页数：12

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