Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes
<p dir="ltr">In this work, both the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been reconfigured to monitor processes using a Bayesian approach. Our construction of these charts are informed by posterior and posterior predictive distri...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513546047455232 |
|---|---|
| author | Chelsea L. Jones (21606239) |
| author2 | Abdel‐Salam G. Abdel‐Salam (14777080) D'Arcy Mays (21606242) |
| author2_role | author author |
| author_facet | Chelsea L. Jones (21606239) Abdel‐Salam G. Abdel‐Salam (14777080) D'Arcy Mays (21606242) |
| author_role | author |
| dc.creator.none.fl_str_mv | Chelsea L. Jones (21606239) Abdel‐Salam G. Abdel‐Salam (14777080) D'Arcy Mays (21606242) |
| dc.date.none.fl_str_mv | 2022-11-17T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/qre.3229 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Novel_Bayesian_CUSUM_and_EWMA_control_charts_via_various_loss_functions_for_monitoring_processes/29413373 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Manufacturing engineering Mathematical sciences Statistics Bayesian control charts CUSUM chart EWMA chart Posterior predictive distribution Statistical process control (SPC) Process monitoring |
| dc.title.none.fl_str_mv | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In this work, both the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been reconfigured to monitor processes using a Bayesian approach. Our construction of these charts are informed by posterior and posterior predictive distributions found using three loss functions: the squared error, precautionary, and linex. We use these control charts on count data, performing a simulation study to assess chart performance. Our simulations consist of sensitivity analysis of the out‐of‐control shift size and choice of hyper‐parameters of the given distributions. Practical use of theses charts are evaluated on real data.</p><h2>Other Information</h2><p dir="ltr">Published in: Quality and Reliability Engineering International<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/qre.3229" target="_blank">https://dx.doi.org/10.1002/qre.3229</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d9c3a6e042adfe439e82986145f5d853 |
| identifier_str_mv | 10.1002/qre.3229 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29413373 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processesChelsea L. Jones (21606239)Abdel‐Salam G. Abdel‐Salam (14777080)D'Arcy Mays (21606242)EngineeringManufacturing engineeringMathematical sciencesStatisticsBayesian control chartsCUSUM chartEWMA chartPosterior predictive distributionStatistical process control (SPC)Process monitoring<p dir="ltr">In this work, both the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been reconfigured to monitor processes using a Bayesian approach. Our construction of these charts are informed by posterior and posterior predictive distributions found using three loss functions: the squared error, precautionary, and linex. We use these control charts on count data, performing a simulation study to assess chart performance. Our simulations consist of sensitivity analysis of the out‐of‐control shift size and choice of hyper‐parameters of the given distributions. Practical use of theses charts are evaluated on real data.</p><h2>Other Information</h2><p dir="ltr">Published in: Quality and Reliability Engineering International<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/qre.3229" target="_blank">https://dx.doi.org/10.1002/qre.3229</a></p>2022-11-17T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/qre.3229https://figshare.com/articles/journal_contribution/Novel_Bayesian_CUSUM_and_EWMA_control_charts_via_various_loss_functions_for_monitoring_processes/29413373CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294133732022-11-17T09:00:00Z |
| spellingShingle | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes Chelsea L. Jones (21606239) Engineering Manufacturing engineering Mathematical sciences Statistics Bayesian control charts CUSUM chart EWMA chart Posterior predictive distribution Statistical process control (SPC) Process monitoring |
| status_str | publishedVersion |
| title | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| title_full | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| title_fullStr | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| title_full_unstemmed | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| title_short | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| title_sort | Novel Bayesian CUSUM and EWMA control charts via various loss functions for monitoring processes |
| topic | Engineering Manufacturing engineering Mathematical sciences Statistics Bayesian control charts CUSUM chart EWMA chart Posterior predictive distribution Statistical process control (SPC) Process monitoring |