The GLRT for statistical process control of autocorrelated processes
dc.contributor.author | Apley, Daniel W. | en_US |
dc.contributor.author | Shi, Jianjun | en_US |
dc.date.accessioned | 2006-09-11T17:09:01Z | |
dc.date.available | 2006-09-11T17:09:01Z | |
dc.date.issued | 1999-12 | en_US |
dc.identifier.citation | APLEY, DANIEL W.; SHI, JIANJUN; (1999). "The GLRT for statistical process control of autocorrelated processes." IIE Transactions 31(12): 1123-1134. <http://hdl.handle.net/2027.42/45916> | en_US |
dc.identifier.issn | 0740-817X | en_US |
dc.identifier.issn | 1573-9724 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45916 | |
dc.description.abstract | This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests – a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the GLRT performance is far superior to either a CUSUM or Shewhart test, while for others the difference is negligible or the CUSUM test performs slightly better. Simple, intuitive guidelines are provided for determining which residual-based test to use. Additional advantages of the GLRT are that it directly provides estimates of the magnitude and time of occurrence of the mean shift, and can be used to distinguish different types of faults, e.g., a sustained mean shift versus a temporary spike. | en_US |
dc.format.extent | 190464 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Engineering | en_US |
dc.subject.other | Mechanical Engineering | en_US |
dc.title | The GLRT for statistical process control of autocorrelated processes | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbsecondlevel | Management | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI, 48109-2117, USA | en_US |
dc.contributor.affiliationother | Department of Industrial Engineering, Texas A & M University, College Station, TX, 77843-3131, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45916/1/10756_2004_Article_242664.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1007692012244 | en_US |
dc.identifier.source | IIE Transactions | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.