Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data

Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data

In this paper, the parameter estimation is discussed by using the maximum likelihood method when the available data have the form of progressively censored sample from a constant-stress accelerated competing failure model. Normal approximation and bootstrap confidence intervals for the unknown param...

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Main Authors: Wang Ying, Yan Zai-Zai
Format: Article
Language:English
Published: VINCA Institute of Nuclear Sciences 2021-01-01
Series:Thermal Science
Subjects:
constant-stress accelerated competing failure model
thermal stress
inverse weibull distribution
markov chain monte-carlo method
Online Access:http://www.doiserbia.nb.rs/img/doi/0354-9836/2021/0354-98362100097W.pdf
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spelling doaj-4397de138e67448fae059ba24c17d9d42021-05-27T13:12:06ZengVINCA Institute of Nuclear SciencesThermal Science0354-98362334-71632021-01-01253 Part B2127213410.2298/TSCI191226097W0354-98362100097WStatistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored dataWang Ying0Yan Zai-Zai1Science College, Inner Mongolia University of Technology, Hohhot, ChinaScience College, Inner Mongolia University of Technology, Hohhot, ChinaIn this paper, the parameter estimation is discussed by using the maximum likelihood method when the available data have the form of progressively censored sample from a constant-stress accelerated competing failure model. Normal approximation and bootstrap confidence intervals for the unknown parameters are obtained and compared numerically. The simulation results show that bootstrap confidence intervals perform better than normal approximation. A thermal stress example is discussed.http://www.doiserbia.nb.rs/img/doi/0354-9836/2021/0354-98362100097W.pdfconstant-stress accelerated competing failure modelthermal stressinverse weibull distributionmarkov chain monte-carlo method
collection DOAJ
language English
format Article
sources DOAJ
author Wang Ying
Yan Zai-Zai
spellingShingle Wang Ying
Yan Zai-Zai
Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
Thermal Science
constant-stress accelerated competing failure model
thermal stress
inverse weibull distribution
markov chain monte-carlo method
author_facet Wang Ying
Yan Zai-Zai
author_sort Wang Ying
title Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
title_short Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
title_full Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
title_fullStr Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
title_full_unstemmed Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data
title_sort statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-ii censored data
publisher VINCA Institute of Nuclear Sciences
series Thermal Science
issn 0354-9836
2334-7163
publishDate 2021-01-01
description In this paper, the parameter estimation is discussed by using the maximum likelihood method when the available data have the form of progressively censored sample from a constant-stress accelerated competing failure model. Normal approximation and bootstrap confidence intervals for the unknown parameters are obtained and compared numerically. The simulation results show that bootstrap confidence intervals perform better than normal approximation. A thermal stress example is discussed.
topic constant-stress accelerated competing failure model
thermal stress
inverse weibull distribution
markov chain monte-carlo method
url http://www.doiserbia.nb.rs/img/doi/0354-9836/2021/0354-98362100097W.pdf
work_keys_str_mv AT wangying statisticalinferenceontheacceleratedcompetingfailuremodelfromtheinverseweibulldistributionunderprogressivelytypeiicensoreddata
AT yanzaizai statisticalinferenceontheacceleratedcompetingfailuremodelfromtheinverseweibulldistributionunderprogressivelytypeiicensoreddata
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