The Performance of Multivariate Quality-Control Charts for Autocorrelated Bivariate Data
| dc.contributor.advisor | Zantek, Paul | en_US |
| dc.contributor.author | Wu, Chen-Hsiang | en_US |
| dc.contributor.department | Applied Mathematics and Scientific Computation | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2006-09-12T05:38:10Z | |
| dc.date.available | 2006-09-12T05:38:10Z | |
| dc.date.issued | 2006-06-02 | en_US |
| dc.description.abstract | To monitor the mass production process, several quality control charts are constructed. Two of the most recognized schemes are the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) schemes. Originally, we assume that the observations from the production process are independent. However, sometimes the observations are autocorrelated. In this article, a vector autoregressive model VAR (m) is applied. Here we want to study the impact of autocorrelations on both schemes. We also want to know about which scheme is more efficient when the observations are autocorrelated. | en_US |
| dc.format.extent | 794587 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/3738 | |
| dc.language.iso | en_US | |
| dc.subject.pqcontrolled | Applied Mechanics | en_US |
| dc.title | The Performance of Multivariate Quality-Control Charts for Autocorrelated Bivariate Data | en_US |
| dc.type | Thesis | en_US |
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