On Detection of Changes in Count Data

The problem of monitoring categorical data is encountered in a wide range of practical settings. For example, in the field of manufacturing, problems of this type include monitoring defect rates, levels of contamination or data integrity. This article discusses an approach to monitoring in situations where the underlying parameters of the categorical data are subject to abrupt changes of unpredictable magnitude at some unknown points in time. We derive detection schemes based on the Likelihood Ratio Approach and discuss their performance and issues related to their design and analysis. The paper also discusses problems related to on-line estimation of the parameters in the presence of abrupt changes. It contains several examples that illustrate use of the proposed techniques in semiconductor industry.

By: Emmanuel Yashchin

Published in: RC23383 in 2004


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