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Analyzing stress-strength reliability δ=P[U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring
This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function δ = P ( U < V < W ) , where...
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Published in: | International journal of system assurance engineering and management 2024, Vol.15 (6), p.2453-2472 |
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container_title | International journal of system assurance engineering and management |
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creator | Nayal, Amit Singh Singh, Bhupendra Tripathi, Vrijesh Tyagi, Abhishek |
description | This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function
δ
=
P
(
U
<
V
<
W
)
, where
V
denotes the system’s strength, and
U
and
W
represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables
U
,
V
, and
W
as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of
δ
have been obtained. In the Bayesian paradigm, the Bayes estimator of
δ
under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for
δ
are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory. |
doi_str_mv | 10.1007/s13198-024-02264-4 |
format | article |
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δ
=
P
(
U
<
V
<
W
)
, where
V
denotes the system’s strength, and
U
and
W
represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables
U
,
V
, and
W
as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of
δ
have been obtained. In the Bayesian paradigm, the Bayes estimator of
δ
under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for
δ
are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory.</description><identifier>ISSN: 0975-6809</identifier><identifier>EISSN: 0976-4348</identifier><identifier>DOI: 10.1007/s13198-024-02264-4</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Asymptotic methods ; Bayesian analysis ; Confidence intervals ; Engineering ; Engineering Economics ; Logistics ; Marketing ; Markov chains ; Maximum likelihood estimators ; Monte Carlo simulation ; Organization ; Original Article ; Quality Control ; Random variables ; Reliability ; Reliability analysis ; Safety and Risk ; Statistical analysis ; Stresses ; System reliability</subject><ispartof>International journal of system assurance engineering and management, 2024, Vol.15 (6), p.2453-2472</ispartof><rights>The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-p227t-81d033558f671b93d6bc3f16df1970a5b6eafdce0070b6e7c6217d10261923c23</cites><orcidid>0000-0001-5538-8805</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Nayal, Amit Singh</creatorcontrib><creatorcontrib>Singh, Bhupendra</creatorcontrib><creatorcontrib>Tripathi, Vrijesh</creatorcontrib><creatorcontrib>Tyagi, Abhishek</creatorcontrib><title>Analyzing stress-strength reliability δ=P[U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring</title><title>International journal of system assurance engineering and management</title><addtitle>Int J Syst Assur Eng Manag</addtitle><description>This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function
δ
=
P
(
U
<
V
<
W
)
, where
V
denotes the system’s strength, and
U
and
W
represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables
U
,
V
, and
W
as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of
δ
have been obtained. In the Bayesian paradigm, the Bayes estimator of
δ
under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for
δ
are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory.</description><subject>Asymptotic methods</subject><subject>Bayesian analysis</subject><subject>Confidence intervals</subject><subject>Engineering</subject><subject>Engineering Economics</subject><subject>Logistics</subject><subject>Marketing</subject><subject>Markov chains</subject><subject>Maximum likelihood estimators</subject><subject>Monte Carlo simulation</subject><subject>Organization</subject><subject>Original Article</subject><subject>Quality Control</subject><subject>Random variables</subject><subject>Reliability</subject><subject>Reliability analysis</subject><subject>Safety and Risk</subject><subject>Statistical analysis</subject><subject>Stresses</subject><subject>System reliability</subject><issn>0975-6809</issn><issn>0976-4348</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNpFkdtKw0AQhoMoWGpfwKsFr1dnD9kkUi_a4qFQ0ItWBZGwSTZ1S9nG3Y1Sn8QH8Tl8JjdW8GKYgfmY-Wf-KDomcEoAkjNHGMlSDJSHoIJjvhf1IEsE5oyn-791jEUK2WE0cG4FAIQSTjn0os-RkevthzZL5LxVzuEumaV_QVattSz0Wvst-v66uHtaDO-HD8_nSKKx3CqnpUHSVKi26rVVxmvnUaOsa1Tp9ZtC7zoMGbfW4sfpFFWhbXXRer0xqDWVsqixm2W3soPn20bhgJXKuI0Nco6ig1qunRr85X60uLqcT27w7PZ6OhnNcENp4nFKKmAsjtNaJKTIWCWKktVEVDXJEpBxIZSsq1KFP0Gok1JQklQEqCAZZSVl_ehkNzeoCWc4n682rQ1PcTkDkXIeU0gCxXaUazpxyv5TBPLOhXznQh5cyH9dyDn7AZNtfZY</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Nayal, Amit Singh</creator><creator>Singh, Bhupendra</creator><creator>Tripathi, Vrijesh</creator><creator>Tyagi, Abhishek</creator><general>Springer India</general><general>Springer Nature B.V</general><scope/><orcidid>https://orcid.org/0000-0001-5538-8805</orcidid></search><sort><creationdate>2024</creationdate><title>Analyzing stress-strength reliability δ=P[U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring</title><author>Nayal, Amit Singh ; Singh, Bhupendra ; Tripathi, Vrijesh ; Tyagi, Abhishek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p227t-81d033558f671b93d6bc3f16df1970a5b6eafdce0070b6e7c6217d10261923c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Asymptotic methods</topic><topic>Bayesian analysis</topic><topic>Confidence intervals</topic><topic>Engineering</topic><topic>Engineering Economics</topic><topic>Logistics</topic><topic>Marketing</topic><topic>Markov chains</topic><topic>Maximum likelihood estimators</topic><topic>Monte Carlo simulation</topic><topic>Organization</topic><topic>Original Article</topic><topic>Quality Control</topic><topic>Random variables</topic><topic>Reliability</topic><topic>Reliability analysis</topic><topic>Safety and Risk</topic><topic>Statistical analysis</topic><topic>Stresses</topic><topic>System reliability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nayal, Amit Singh</creatorcontrib><creatorcontrib>Singh, Bhupendra</creatorcontrib><creatorcontrib>Tripathi, Vrijesh</creatorcontrib><creatorcontrib>Tyagi, Abhishek</creatorcontrib><jtitle>International journal of system assurance engineering and management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nayal, Amit Singh</au><au>Singh, Bhupendra</au><au>Tripathi, Vrijesh</au><au>Tyagi, Abhishek</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analyzing stress-strength reliability δ=P[U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring</atitle><jtitle>International journal of system assurance engineering and management</jtitle><stitle>Int J Syst Assur Eng Manag</stitle><date>2024</date><risdate>2024</risdate><volume>15</volume><issue>6</issue><spage>2453</spage><epage>2472</epage><pages>2453-2472</pages><issn>0975-6809</issn><eissn>0976-4348</eissn><abstract>This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function
δ
=
P
(
U
<
V
<
W
)
, where
V
denotes the system’s strength, and
U
and
W
represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables
U
,
V
, and
W
as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of
δ
have been obtained. In the Bayesian paradigm, the Bayes estimator of
δ
under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for
δ
are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory.</abstract><cop>New Delhi</cop><pub>Springer India</pub><doi>10.1007/s13198-024-02264-4</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0001-5538-8805</orcidid></addata></record> |
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language | eng |
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source | Springer Nature |
subjects | Asymptotic methods Bayesian analysis Confidence intervals Engineering Engineering Economics Logistics Marketing Markov chains Maximum likelihood estimators Monte Carlo simulation Organization Original Article Quality Control Random variables Reliability Reliability analysis Safety and Risk Statistical analysis Stresses System reliability |
title | Analyzing stress-strength reliability δ=P[U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring |
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