The development of a destructive sampling method designed for high quality production processes (DSM-HQ)

Delgadillo, Francisco; Bremer, Ronald

doi:10.1007/s11135-009-9222-2

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…In addition, the EB method was also applied to estimate the percentage of defective units when the lots were accepted [24]. Ohta and Ogawa [25], Shin and Shin [26], Karunamuni [27] utilized the EB approach, combined with a specified cost function, for testing destructive items of high quality products in Poisson process.…”

Section: Introductionmentioning

confidence: 99%

Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan

et al. 2020

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An acceptance sampling plans are statistical tools in quality control which often used for lot inspection in several areas such as industry, engineering and business. It can be applied for preserving the quality of products in industry process and preserving the producer's risk and consumer's risk in the production process of manufactures. The objective of this study is to utilize the Empirical Bayes approach based on squared error loss and precautionary loss functions for parameter estimation in sequential sampling plans. The parameters are estimated using Lindley's approximation technique, and hyper-parameters can be obtained via Gibbs sampling technique. Data are normally distributed under an unknown mean and variance. The proposed plans are compared with traditional approaches including a single sampling plan and sequential sampling plan. The probability of acceptance (P a) and average sample number (ASN) are used as criterion for comparison. Results show that the proposed plans yielded the smaller ASN and higher P a than both classical methods.

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Section: Introductionmentioning

confidence: 99%

Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan

et al. 2020

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“…The minimum EB risk was used to determine the stopping time in the sequential decision. Delgadillo and Bremer [25] utilized the EB approach, combined with a specified cost function, to test the destruction of high-quality products in a Poisson process. The proposed method was compared with traditional methods, including single, double, multiple, and skip-lot sampling plans.…”

Section: Introductionmentioning

confidence: 99%

Empirical Bayes Prediction in a Sequential Sampling Plan Based on Loss Functions

et al. 2019

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The application of empirical Bayes for lot inspection in sequential sampling plans is usually conducted to estimate the proportion of defective items in the lot rather than for hypothesis testing of the variables' process mean. In this paper, we propose the use of empirical Bayes in a sequential sampling plan variables' process mean testing under a squared error loss function and precautionary loss function, for which the prediction is performed to estimate a sequence of the mean when the data are normally distributed in the case of a known mean and unknown variance. The proposed plans are compared with the sequential sampling plan. The proposed techniques yielded smaller average sample number (ASN) and provided higher probability of acceptance (P a ) than the sequential sampling plan.

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The development of a destructive sampling method designed for high quality production processes (DSM-HQ)

Abstract: Destructive sampling, High quality, Monitoring,

Cited by 2 publications

References 22 publications

Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan

Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan

Empirical Bayes Prediction in a Sequential Sampling Plan Based on Loss Functions

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