Control chart for geometrically distributed data based on Bayesian fast double bootstrap
Accurate parameter estimation is a critical component of effective process control using g charts. While traditional methods like maximum likelihood and Bayesian estimation are widely used, th ey may exhibit limitations in small sample size scenarios, leading to inaccurate parameter estimates. To ad...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125001530 |
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| author | Muhammad Yahya Matdoan Muhammad Mashuri Muhammad Ahsan |
| author_facet | Muhammad Yahya Matdoan Muhammad Mashuri Muhammad Ahsan |
| author_sort | Muhammad Yahya Matdoan |
| collection | DOAJ |
| description | Accurate parameter estimation is a critical component of effective process control using g charts. While traditional methods like maximum likelihood and Bayesian estimation are widely used, th ey may exhibit limitations in small sample size scenarios, leading to inaccurate parameter estimates. To address these challenges, minimum variance unbiased (MVU) estimators have been developed. For specific conditions, such as limited data and no nonconforming items, bootstrap-based Bayesian estimators offer a computational alternative. However, these estimators may struggle to detect significant process shifts, particularly in the presence of large deviations. This research introduces a novel Bayesian fast double bootstrap approach for parameter estimation in g-charts. By efficiently handling small sample sizes and effectively detecting large process shifts, this method aims to significantly enhance the accuracy and reliability of process monitoring. The proposed approach leverages the strengths of both bootstrap and double bootstrap techniques, while addressing their limitations through a computationally efficient algorithm. This advancement is expected to contribute to improved process control and quality assurance in various industrial applications. Key points: • A Bayesian fast double bootstrap (BFDB) approach was developed for parameter estimation in process monitoring, particularly for small sample sizes. Comparative analysis with minimum variance unbiased (MVU) estimators demonstrated the superior sensitivity and computational efficiency of BFDB for process monitoring • A comparative analysis of BFDB and MVU parameter estimation methods revealed that BFDB consistently outperformed MVU in high-quality process monitoring scenarios. |
| format | Article |
| id | doaj-art-457c2bbf9e55482f83bf5256aefeb436 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-457c2bbf9e55482f83bf5256aefeb4362025-08-20T03:24:48ZengElsevierMethodsX2215-01612025-06-011410330710.1016/j.mex.2025.103307Control chart for geometrically distributed data based on Bayesian fast double bootstrapMuhammad Yahya Matdoan0Muhammad Mashuri1Muhammad Ahsan2Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya 60111, Indonesia; Department of Statistics, Faculty of Science and Technology, University of Pattimura, Jl. Ir. M. Putuhena, Kampus Unpatti-Poka, Ambon 97233, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya 60111, Indonesia; Corresponding author.Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya 60111, IndonesiaAccurate parameter estimation is a critical component of effective process control using g charts. While traditional methods like maximum likelihood and Bayesian estimation are widely used, th ey may exhibit limitations in small sample size scenarios, leading to inaccurate parameter estimates. To address these challenges, minimum variance unbiased (MVU) estimators have been developed. For specific conditions, such as limited data and no nonconforming items, bootstrap-based Bayesian estimators offer a computational alternative. However, these estimators may struggle to detect significant process shifts, particularly in the presence of large deviations. This research introduces a novel Bayesian fast double bootstrap approach for parameter estimation in g-charts. By efficiently handling small sample sizes and effectively detecting large process shifts, this method aims to significantly enhance the accuracy and reliability of process monitoring. The proposed approach leverages the strengths of both bootstrap and double bootstrap techniques, while addressing their limitations through a computationally efficient algorithm. This advancement is expected to contribute to improved process control and quality assurance in various industrial applications. Key points: • A Bayesian fast double bootstrap (BFDB) approach was developed for parameter estimation in process monitoring, particularly for small sample sizes. Comparative analysis with minimum variance unbiased (MVU) estimators demonstrated the superior sensitivity and computational efficiency of BFDB for process monitoring • A comparative analysis of BFDB and MVU parameter estimation methods revealed that BFDB consistently outperformed MVU in high-quality process monitoring scenarios.http://www.sciencedirect.com/science/article/pii/S2215016125001530g-Chart Using MVU Estimator and BDFB Estimator |
| spellingShingle | Muhammad Yahya Matdoan Muhammad Mashuri Muhammad Ahsan Control chart for geometrically distributed data based on Bayesian fast double bootstrap MethodsX g-Chart Using MVU Estimator and BDFB Estimator |
| title | Control chart for geometrically distributed data based on Bayesian fast double bootstrap |
| title_full | Control chart for geometrically distributed data based on Bayesian fast double bootstrap |
| title_fullStr | Control chart for geometrically distributed data based on Bayesian fast double bootstrap |
| title_full_unstemmed | Control chart for geometrically distributed data based on Bayesian fast double bootstrap |
| title_short | Control chart for geometrically distributed data based on Bayesian fast double bootstrap |
| title_sort | control chart for geometrically distributed data based on bayesian fast double bootstrap |
| topic | g-Chart Using MVU Estimator and BDFB Estimator |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125001530 |
| work_keys_str_mv | AT muhammadyahyamatdoan controlchartforgeometricallydistributeddatabasedonbayesianfastdoublebootstrap AT muhammadmashuri controlchartforgeometricallydistributeddatabasedonbayesianfastdoublebootstrap AT muhammadahsan controlchartforgeometricallydistributeddatabasedonbayesianfastdoublebootstrap |