A Competing Risks Model With Multiply Censored Reliability Data Under Multivariate Weibull Distributions

A competing risks model is composed of more than one failure mode that naturally arises when reliability systems are made of two or more components. A series system fails if any of its components fail. As these components are all part of the same system, they may be correlated. In this paper, we con...

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Vydáno v:IEEE transactions on reliability Ročník 68; číslo 2; s. 462 - 475
Hlavní autoři: Fan, Tsai-Hung, Wang, Yi-Fu, Ju, She-Kai
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Component reliability
Computer simulation
Data models
Economic models
Expectation–maximization (EM) algorithm
Exponential distribution
Failure modes
Failure times
Fans
Marshall–Olkin Weibull distribution
masked data
Mathematical models
Maximum likelihood estimates
Maximum likelihood estimation
Mean time to failure
multiply censored life test
Multivariate analysis
Parameter estimation
Quantiles
Reliability
series system
Silicon
Statistical analysis
Statistical inference
Weibull distribution
ISSN:0018-9529, 1558-1721
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Abstract A competing risks model is composed of more than one failure mode that naturally arises when reliability systems are made of two or more components. A series system fails if any of its components fail. As these components are all part of the same system, they may be correlated. In this paper, we consider a competing risks model with <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> failure modes and whose lifetimes follow a joint <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-variate Marshall-Olkin Weibull distribution, when the data are multiply censored. Normally, each observation contains the failure time as well as the failure mode. In practice, however, it is common to have masked data in which the component that causes failure of the system is not observed. We apply the maximum likelihood approach via expectation-maximization algorithm, along with the missing information principle, to estimate the parameters and the standard errors of the maximum likelihood estimates. Statistical inference on the model parameters, the mean time to failure, and the quantiles of the failure time of the system as well as of the components are all developed. The proposed method is evaluated by a simulation study and also applied to two two-component real datasets successfully.
AbstractList A competing risks model is composed of more than one failure mode that naturally arises when reliability systems are made of two or more components. A series system fails if any of its components fail. As these components are all part of the same system, they may be correlated. In this paper, we consider a competing risks model with <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> failure modes and whose lifetimes follow a joint <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-variate Marshall-Olkin Weibull distribution, when the data are multiply censored. Normally, each observation contains the failure time as well as the failure mode. In practice, however, it is common to have masked data in which the component that causes failure of the system is not observed. We apply the maximum likelihood approach via expectation-maximization algorithm, along with the missing information principle, to estimate the parameters and the standard errors of the maximum likelihood estimates. Statistical inference on the model parameters, the mean time to failure, and the quantiles of the failure time of the system as well as of the components are all developed. The proposed method is evaluated by a simulation study and also applied to two two-component real datasets successfully.
A competing risks model is composed of more than one failure mode that naturally arises when reliability systems are made of two or more components. A series system fails if any of its components fail. As these components are all part of the same system, they may be correlated. In this paper, we consider a competing risks model with [Formula Omitted] failure modes and whose lifetimes follow a joint [Formula Omitted]-variate Marshall–Olkin Weibull distribution, when the data are multiply censored. Normally, each observation contains the failure time as well as the failure mode. In practice, however, it is common to have masked data in which the component that causes failure of the system is not observed. We apply the maximum likelihood approach via expectation–maximization algorithm, along with the missing information principle, to estimate the parameters and the standard errors of the maximum likelihood estimates. Statistical inference on the model parameters, the mean time to failure, and the quantiles of the failure time of the system as well as of the components are all developed. The proposed method is evaluated by a simulation study and also applied to two two-component real datasets successfully.
Author Ju, She-Kai
Wang, Yi-Fu
Fan, Tsai-Hung
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Cites_doi 10.1016/j.csda.2008.11.009
10.1109/TR.1984.5221828
10.1177/0962280215604510
10.1111/j.2517-6161.1982.tb01203.x
10.2307/2528828
10.1080/00401706.1974.10489147
10.1080/00401706.2012.707579
10.2307/2291285
10.1016/0047-259X(78)90064-7
10.1016/S0096-3003(03)00688-X
10.1515/jem-2015-0005
10.2307/2282907
10.1109/24.9880
10.2307/2532041
10.1109/TR.2016.2515589
10.1109/TR.2014.2337813
10.1109/24.510806
10.1002/0471722065
10.1016/j.csda.2012.05.004
10.1016/S0167-7152(99)00160-1
10.1191/0962280203sm335ra
10.1002/9780470191613
10.1093/biomet/45.3-4.504
10.1007/s00180-018-0804-0
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References ref13
ref12
huster (ref30) 1989; 45
ref15
fan (ref29) 2013
ref31
meeker (ref20) 1998
ref10
ref2
ref1
ref17
ref19
duchateau (ref16) 2008
ref18
mukhopadhyay (ref6) 1993
ref24
ref23
ref26
ref25
ref22
ref21
morgenstern (ref14) 1956; 8
ref28
ref8
ref7
ref9
ref4
lee (ref27) 1974
ref3
ref5
nelsen (ref11) 2006
References_xml – ident: ref23
  doi: 10.1016/j.csda.2008.11.009
– ident: ref2
  doi: 10.1109/TR.1984.5221828
– ident: ref18
  doi: 10.1177/0962280215604510
– ident: ref24
  doi: 10.1111/j.2517-6161.1982.tb01203.x
– year: 2006
  ident: ref11
  publication-title: An Introduction to Copulas
– ident: ref1
  doi: 10.2307/2528828
– ident: ref22
  doi: 10.1080/00401706.1974.10489147
– ident: ref17
  doi: 10.1080/00401706.2012.707579
– ident: ref5
  doi: 10.2307/2291285
– ident: ref8
  doi: 10.1016/0047-259X(78)90064-7
– ident: ref9
  doi: 10.1016/S0096-3003(03)00688-X
– volume: 8
  start-page: 234
  year: 1956
  ident: ref14
  article-title: Einfache beispiele zweidimensionaler verteilungen
  publication-title: Mitteilungsblatt für Mathematishe Statistik
– ident: ref19
  doi: 10.1515/jem-2015-0005
– ident: ref21
  doi: 10.2307/2282907
– ident: ref3
  doi: 10.1109/24.9880
– volume: 45
  start-page: 145
  year: 1989
  ident: ref30
  article-title: Modelling paired survival data with covariates
  publication-title: Biometrics
  doi: 10.2307/2532041
– ident: ref15
  doi: 10.1109/TR.2016.2515589
– year: 2013
  ident: ref29
  article-title: Reliability analysis of two-component series system under Marshall-Olkin bivariate Weibull lifetime distribution
– ident: ref10
  doi: 10.1109/TR.2014.2337813
– year: 1993
  ident: ref6
– ident: ref4
  doi: 10.1109/24.510806
– ident: ref26
  doi: 10.1002/0471722065
– ident: ref25
  doi: 10.1016/j.csda.2012.05.004
– ident: ref7
  doi: 10.1016/S0167-7152(99)00160-1
– ident: ref12
  doi: 10.1191/0962280203sm335ra
– year: 1998
  ident: ref20
  publication-title: Statistical Methods for Reliability Data
– ident: ref28
  doi: 10.1002/9780470191613
– year: 2008
  ident: ref16
  publication-title: The Frailty Model
– start-page: 291
  year: 1974
  ident: ref27
  article-title: Results on failure time and pattern for the series system
  publication-title: Reliability and Biometry Statistical Analysis of Lifelength
– ident: ref31
  doi: 10.1093/biomet/45.3-4.504
– ident: ref13
  doi: 10.1007/s00180-018-0804-0
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SubjectTerms Algorithms
Component reliability
Computer simulation
Data models
Economic models
Expectation–maximization (EM) algorithm
Exponential distribution
Failure modes
Failure times
Fans
Marshall–Olkin Weibull distribution
masked data
Mathematical models
Maximum likelihood estimates
Maximum likelihood estimation
Mean time to failure
multiply censored life test
Multivariate analysis
Parameter estimation
Quantiles
Reliability
series system
Silicon
Statistical analysis
Statistical inference
Weibull distribution
Title A Competing Risks Model With Multiply Censored Reliability Data Under Multivariate Weibull Distributions
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