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...

Full description

Saved in:
Bibliographic Details
Published inJournal of applied statistics Vol. 36; no. 4; pp. 399 - 414
Main Author Weiss, Christian
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.04.2009
Taylor and Francis Journals
Taylor & Francis Ltd
SeriesJournal of Applied Statistics
Subjects
binomial AR models
binomial AR(1) models
Binomial distribution
Case studies
case study
control charts
Process controls
Statistical process control
Statistics
Online AccessGet full text
ISSN0266-4763
1360-0532
DOI10.1080/02664760802468803

Cover

More Information
Summary: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.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0266-4763
1360-0532
DOI:10.1080/02664760802468803