Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan
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...
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doaj-6926fe4ede4240b2adb5dc6ec61dc6da2021-03-30T02:13:52ZengIEEEIEEE Access2169-35362020-01-018514605146910.1109/ACCESS.2020.29798729031396Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling PlanKatechan Jampachaisri0https://orcid.org/0000-0002-8756-3177Khanittha Tinochai1https://orcid.org/0000-0002-7282-5082Saowanit Sukparungsee2https://orcid.org/0000-0001-5248-8173Yupaporn Areepong3https://orcid.org/0000-0002-5103-9867Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, ThailandDepartment of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandAn 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<sub>a</sub>) and average sample number (ASN) are used as criterion for comparison. Results show that the proposed plans yielded the smaller ASN and higher P<sub>a</sub> than both classical methods.https://ieeexplore.ieee.org/document/9031396/Empirical Bayessequential sampling plansingle sampling plansquared error loss functionprecautionary loss function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Katechan Jampachaisri Khanittha Tinochai Saowanit Sukparungsee Yupaporn Areepong |
spellingShingle |
Katechan Jampachaisri Khanittha Tinochai Saowanit Sukparungsee Yupaporn Areepong Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan IEEE Access Empirical Bayes sequential sampling plan single sampling plan squared error loss function precautionary loss function |
author_facet |
Katechan Jampachaisri Khanittha Tinochai Saowanit Sukparungsee Yupaporn Areepong |
author_sort |
Katechan Jampachaisri |
title |
Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan |
title_short |
Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan |
title_full |
Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan |
title_fullStr |
Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan |
title_full_unstemmed |
Empirical Bayes Based on Squared Error Loss and Precautionary Loss Functions in Sequential Sampling Plan |
title_sort |
empirical bayes based on squared error loss and precautionary loss functions in sequential sampling plan |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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<sub>a</sub>) and average sample number (ASN) are used as criterion for comparison. Results show that the proposed plans yielded the smaller ASN and higher P<sub>a</sub> than both classical methods. |
topic |
Empirical Bayes sequential sampling plan single sampling plan squared error loss function precautionary loss function |
url |
https://ieeexplore.ieee.org/document/9031396/ |
work_keys_str_mv |
AT katechanjampachaisri empiricalbayesbasedonsquarederrorlossandprecautionarylossfunctionsinsequentialsamplingplan AT khanitthatinochai empiricalbayesbasedonsquarederrorlossandprecautionarylossfunctionsinsequentialsamplingplan AT saowanitsukparungsee empiricalbayesbasedonsquarederrorlossandprecautionarylossfunctionsinsequentialsamplingplan AT yupapornareepong empiricalbayesbasedonsquarederrorlossandprecautionarylossfunctionsinsequentialsamplingplan |
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