On Detection of Changes in Categorical Data

We consider situations where the observed data is of categorical type and the underlying parameters are subject to abrupt changes of unpredictable magnitude at unknown points in time. We derive change-point detection schemes based on generalized likelihood ratio tests and develop procedures for thei...

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Bibliographic Details
Published inQuality technology & quantitative management Vol. 9; no. 1; pp. 79 - 96
Main Author Yashchin, Emmanuel
Format Journal Article
LanguageEnglish
Published Taylor & Francis 2012
Subjects
Cusum
detection
likelihood ratio
run length
Online AccessGet full text
ISSN1684-3703
1684-3703
DOI10.1080/16843703.2012.11673279

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Summary:We consider situations where the observed data is of categorical type and the underlying parameters are subject to abrupt changes of unpredictable magnitude at unknown points in time. We derive change-point detection schemes based on generalized likelihood ratio tests and develop procedures for their design and analysis. We also discuss problems related to parameter estimation for categorical data in the presence of abrupt changes. We illustrate use of the proposed methodology for fault characterization and monitoring in the semiconductor industry.
ISSN:1684-3703
1684-3703
DOI:10.1080/16843703.2012.11673279