Publication
Quality Technology and Quantitative Management
Paper

On detection of changes in categorical data

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Abstract

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.

Date

09 Feb 2016

Publication

Quality Technology and Quantitative Management

Authors

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