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IOP Conference Series : Materials Science and Engineering
Article . 2021 . Peer-reviewed
License: CC BY
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IOP Conference Series : Materials Science and Engineering
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A Monte Carlo Expectation Maximization Algorithm for Statistical Inference of Weibull Process with Left Censored Data

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Jan 2021Publisher:IOP PublishingJournal:IOP Conference Series: Materials Science and Engineering, volume 1,043, page 52,017 (issn: 1757-8981, eissn: 1757-899X, Copyright policy )
Authors: Jun-Ming Hu; Hong-Zhong Huang;

doi: 10.1088/1757-899x/1043/5/052017

A Monte Carlo Expectation Maximization Algorithm for Statistical Inference of Weibull Process with Left Censored Data

Abstract

AbstractThe Weibull process plays an important role in the failure analysis of repairable systems. In practice, there exists a situation that the data collected are incomplete. Some of the failure data are missing due to various reasons. Statistical inferences of a Weibull process with incomplete data using Monte Carlo expectation maximization algorithm is proposed. The estimation procedures are derived. A case study is performed to illustrate and compare the performances of this algorithm. It is observed that this method is effective and can simplify the estimation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
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