Bayesian sampling plan for the exponential distribution with generalized Type - II hybrid censoring scheme

Prajapati, Deepak; Mitra, Sharmishtha; Kundu, Debasis

doi:10.1080/03610918.2020.1861293

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bayesian sampling plans necessitate the explicit specification of the distribution from lot to lot, which is referred to as prior distribution. Hafeez and Aziz (2019), Wang and Park (2020), Prajapati et al (2020), Chen et al (2021a), Chen et al (2021b) and Hafeez et al (2022) are a few recent studies that deal with the Bayesian approach. Hafeez and Aziz (2019) consider the quality variation for GChSP using the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Two-Sided Complete Group Chain Sampling Plan (BT-SCGChSP) for Binomial Distribution Using Beta Prior Through Quality Regions

Hafeez

Aziz

2021

Journal of Information and Communication Technology

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%