Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions

Acceptance sampling is a technique for statistical quality assurance based on the inspection of a random sample to decide the lot disposition: accept or reject. Producer’s risk and consumer’s risk are inevitable in acceptance sampling. Most conventional plans only focus on minimizing the consumer’s...

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Autores principales:	Waqar Hafeez, Nazrina Aziz
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2021
Materias:	acceptance sampling bayesian group chain beta distribution binomial distribution quality region Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/1fff3f90c60d45aa870fff5c71454de4
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spelling	oai:doaj.org-article:1fff3f90c60d45aa870fff5c71454de42021-11-14T08:28:07ZBayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions10.32890/jict2022.21.1.31675-414X2180-3862https://doaj.org/article/1fff3f90c60d45aa870fff5c71454de42021-11-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2022.21.1.3https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Acceptance sampling is a technique for statistical quality assurance based on the inspection of a random sample to decide the lot disposition: accept or reject. Producer’s risk and consumer’s risk are inevitable in acceptance sampling. Most conventional plans only focus on minimizing the consumer’s risk. This study focused on minimizing both producer’s and consumer’s risks through the quality region. Experts from available historical knowledge concurred that Bayesian is the best approach to make the correct decision. In this study, a Bayesian two-sided complete group chain sampling plan (BTSCGChSP) was proposed for the average probability of acceptance. The binomial distribution was used to derive the probability of lot acceptance, and the beta distribution was used as the prior distribution. For selected design parameters in BTSCGChSP, the acceptable quality level and limiting quality level were considered to estimate quality regions that were directly associated with producer’s and consumer’s risks, respectively. Four quality regions: (i) quality decision region , (ii) probabilistic quality region (PQR), (iii) limiting quality region, and (iv) indifference quality region, were evaluated. To compare with the existing Bayesian group chain sampling plan (BGChSP), operating characteristic curves were used for the same parameter values and probability of lot acceptance. The findings explained that BTSCGChSP provided a smaller proportion of defectives than BGChSP for the same probability of acceptance. If quality regions were found for the same values of consumer and producer risks, then the BTSCGChSP region would contain fewer defectives than in the BGChSP region. Therefore, for industrial practitioners, the proposed plan is a better substitute for existing BGChSP and other conventional plans. Waqar HafeezNazrina AzizUUM Pressarticleacceptance samplingbayesian group chainbeta distributionbinomial distributionquality regionInformation technologyT58.5-58.64ENJournal of ICT, Vol 21, Iss 1, Pp 51-69 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acceptance sampling bayesian group chain beta distribution binomial distribution quality region Information technology T58.5-58.64
spellingShingle	acceptance sampling bayesian group chain beta distribution binomial distribution quality region Information technology T58.5-58.64 Waqar Hafeez Nazrina Aziz Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
description	Acceptance sampling is a technique for statistical quality assurance based on the inspection of a random sample to decide the lot disposition: accept or reject. Producer’s risk and consumer’s risk are inevitable in acceptance sampling. Most conventional plans only focus on minimizing the consumer’s risk. This study focused on minimizing both producer’s and consumer’s risks through the quality region. Experts from available historical knowledge concurred that Bayesian is the best approach to make the correct decision. In this study, a Bayesian two-sided complete group chain sampling plan (BTSCGChSP) was proposed for the average probability of acceptance. The binomial distribution was used to derive the probability of lot acceptance, and the beta distribution was used as the prior distribution. For selected design parameters in BTSCGChSP, the acceptable quality level and limiting quality level were considered to estimate quality regions that were directly associated with producer’s and consumer’s risks, respectively. Four quality regions: (i) quality decision region , (ii) probabilistic quality region (PQR), (iii) limiting quality region, and (iv) indifference quality region, were evaluated. To compare with the existing Bayesian group chain sampling plan (BGChSP), operating characteristic curves were used for the same parameter values and probability of lot acceptance. The findings explained that BTSCGChSP provided a smaller proportion of defectives than BGChSP for the same probability of acceptance. If quality regions were found for the same values of consumer and producer risks, then the BTSCGChSP region would contain fewer defectives than in the BGChSP region. Therefore, for industrial practitioners, the proposed plan is a better substitute for existing BGChSP and other conventional plans.
format	article
author	Waqar Hafeez Nazrina Aziz
author_facet	Waqar Hafeez Nazrina Aziz
author_sort	Waqar Hafeez
title	Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
title_short	Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
title_full	Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
title_fullStr	Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
title_full_unstemmed	Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions
title_sort	bayesian two-sided complete group chain sampling plan for binomial distribution using beta prior through quality regions
publisher	UUM Press
publishDate	2021
url	https://doaj.org/article/1fff3f90c60d45aa870fff5c71454de4
work_keys_str_mv	AT waqarhafeez bayesiantwosidedcompletegroupchainsamplingplanforbinomialdistributionusingbetapriorthroughqualityregions AT nazrinaaziz bayesiantwosidedcompletegroupchainsamplingplanforbinomialdistributionusingbetapriorthroughqualityregions
_version_	1718429763560800256

Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions

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