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Monitoring of Multiple Binary Data Streams using a Hierarchical Model Structure
Author(s) -
Das Devashish,
Chen Yong,
Zhou Shiyu,
Sievenpiper Crispian
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1831
Subject(s) - cusum , computer science , chart , data mining , data stream mining , binary data , hierarchical database model , binary number , streams , statistics , mathematics , computer network , arithmetic
Multiple streams of binary data occur commonly in practice. In this paper, we propose a hierarchical statistical model to describe multi‐stream binary data that demonstrate over‐dispersion. In such a model, a group of binary streams in a multi‐stream dataset is modeled by a beta‐binominal hierarchical mixture distribution. Using this hierarchical model structure, a cumulative sum (CUSUM) chart based on the log‐likelihood ratio is developed to monitor all the data streams simultaneously. The performance of the CUSUM chart is investigated and compared to conventional monitoring schemes through numerical studies and a real‐world dataset. It is shown that the CUSUM method using the hierarchical model is effective and advantageous over the conventional methods. Copyright © 2015 John Wiley & Sons, Ltd.