Monitoring correlated processes with binomial marginals
Few approaches for monitoring autocorrelated attribute data have been proposed in the literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and well-interpretable process model may be adequate. Based on known and newly derived statistical properti...
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| Published in | Journal of applied statistics Vol. 36; no. 4; pp. 399 - 414 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
01.04.2009
Taylor and Francis Journals Taylor & Francis Ltd |
| Series | Journal of Applied Statistics |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-4763 1360-0532 |
| DOI | 10.1080/02664760802468803 |
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| Abstract | Few approaches for monitoring autocorrelated attribute data have been proposed in the literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and well-interpretable process model may be adequate. Based on known and newly derived statistical properties of this model, we shall develop approaches to monitor a binomial AR(1) process, and investigate their performance in a simulation study. A case study demonstrates the applicability of the binomial AR(1) model and of the proposed control charts to problems from statistical process control. |
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| AbstractList | Few approaches for monitoring autocorrelated attribute data have been proposed in the literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and well-interpretable process model may be adequate. Based on known and newly derived statistical properties of this model, we shall develop approaches to monitor a binomial AR(1) process, and investigate their performance in a simulation study. A case study demonstrates the applicability of the binomial AR(1) model and of the proposed control charts to problems from statistical process control. Few approaches for monitoring autocorrelated attribute data have been proposed in the literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and well-interpretable process model may be adequate. Based on known and newly derived statistical properties of this model, we shall develop approaches to monitor a binomial AR(1) process, and investigate their performance in a simulation study. A case study demonstrates the applicability of the binomial AR(1) model and of the proposed control charts to problems from statistical process control. [PUBLICATION ABSTRACT] |
| Author | Weiß, Christian H. |
| Author_xml | – fullname: Weiss, Christian |
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| Title | Monitoring correlated processes with binomial marginals |
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