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A comparison of the modified likelihood-ratio-test-based shewhart and EWMA control charts for monitoring binary profiles

  • Chao Yin*
  • , Yihai He
  • , Zhen Shen
  • , Chun Hui Wu
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Profile monitoring is used to check and evaluate the stability of the functional relationship between a response variable and one or more explanatory variables known as profile over time. Many studies assume that the response variable follows a continuous and normal distribution, while in fact it could be discrete, for example binary profiles. However, at present, there are few researches in this field. Based on an in-control binary dataset, this paper uses the logistic regression model to estimate the parameters in Phase I. And in Phase II, we apply bi-sectional search method to modifying the UCL's calculation of the likelihood-ratio-test-based Shewhart and EWMA control charts. Moreover, according to the estimated parameters, ARL's performances of the two modified control charts under different parameters' deviation are compared.

Original languageEnglish
Title of host publication19th International Conference on Industrial Engineering and Engineering Management
Subtitle of host publicationAssistive Technology of Industrial Engineering
PublisherSpringer Science and Business Media Deutschland GmbH
Pages23-29
Number of pages7
ISBN (Print)9783642383908
DOIs
StatePublished - 2013
Event19th International Conference on Industrial Engineering and Engineering Management: Assistive Technology of Industrial Engineering - Changsha, China
Duration: 27 Oct 201229 Oct 2012

Publication series

Name19th International Conference on Industrial Engineering and Engineering Management: Assistive Technology of Industrial Engineering

Conference

Conference19th International Conference on Industrial Engineering and Engineering Management: Assistive Technology of Industrial Engineering
Country/TerritoryChina
CityChangsha
Period27/10/1229/10/12

Keywords

  • ARL
  • Bi-sectional search
  • Binary profile
  • Logistic regression model

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