.png){kind=icon}
返回
EM algorithm for one-shot device testing with competing risks under exponential distribution
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2014.12.014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper provides an extension of the work of Balakrishnan and Ling [1] by introducing a competing risks model into a one-shot device testing analysis under an accelerated life test setting. An Expectation Maximization (EM) algorithm is then developed for the estimation of the model parameters. An extensive Monte Carlo simulation study is carried out to assess the performance of the EM algorithm and then compare the obtained results with the initial estimates obtained by the Inequality Constrained Least Squares (ICLS) method of estimation. Finally, we apply the EM algorithm to a clinical data, ED01, to illustrate the method of inference developed here. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
EM algorithm
Inequality constrained least squares
One-shot device
Competing risks
Masked data
Exponential distribution
ED01 Data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
引用论文
Addendum: Preventing the return of fear in humans using reconsolidation update mechanisms.png)
Nature
OAAI
NatureIF0
OAAI分享收藏
Permeability and permselectivity of gases in fluorinated and non-fluorinated polyimidesPolymer
PolymerIF0
PREAI
分享收藏Multiple-Stress Model for One-Shot Device Testing Data Under Exponential Distribution指数分布下一次性器件测试数据的多应力模型
PREAI
分享收藏
查看更多内容