Statistical process monitoring using an empirical Bayes multivariate process control chart
| dc.citation.epage | 124 | en_US |
| dc.citation.issue | 2 | en_US |
| dc.citation.spage | 119 | en_US |
| dc.citation.volume | 17 | en_US |
| dc.citation.woscount | 3 | |
| dc.contributor.author | Feltz, CJ | en_US |
| dc.contributor.author | Shiau, JJH | en_US |
| dc.contributor.department | 統計學研究所 | zh_TW |
| dc.contributor.department | Institute of Statistics | en_US |
| dc.date.accessioned | 2014-12-08T15:44:09Z | |
| dc.date.available | 2014-12-08T15:44:09Z | |
| dc.date.issued | 2001-03-01 | en_US |
| dc.description.abstract | In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the, data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory: we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright (C) 2001 John Wiley & Sons, Ltd. | en_US |
| dc.identifier.doi | 10.1002/qre.393 | en_US |
| dc.identifier.issn | 0748-8017 | en_US |
| dc.identifier.journal | QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1002/qre.393 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/29810 | |
| dc.identifier.wosnumber | WOS:000168447800007 | |
| dc.language.iso | en_US | en_US |
| dc.subject | multivariate | en_US |
| dc.subject | quality control | en_US |
| dc.subject | on-line | en_US |
| dc.title | Statistical process monitoring using an empirical Bayes multivariate process control chart | en_US |
| dc.type | Article | en_US |