A Markov-based control chart for dependent binary data

There are different statistical approaches for monitoring proportion when the observations are binary. Usually, it is considered that the data are independent. But there are situations in which the data are intrinsically correlated. In this paper, two Markov-based charts, the Markov EWMA and the Mar...

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Bibliographic Details
Published in2011 IEEE International Conference on Quality and Reliability pp. 288 - 291
Main Authors Dokouhaki, P., Noorossana, R., Fatahi, A. A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
ARL
binary observations
control chart
Control charts
Correlation
dependent data
Inspection
Markov chain
Markov processes
Mathematical model
Monitoring
Process control
proportion
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Summary:There are different statistical approaches for monitoring proportion when the observations are binary. Usually, it is considered that the data are independent. But there are situations in which the data are intrinsically correlated. In this paper, two Markov-based charts, the Markov EWMA and the Markov Shewhart, are presented as reasonable charts for dependent binary observations and a new method named Markov CUSUM chart is developed. It is shown that the one-sided Markov CUSUM chart has better performance than the two other charts in most situations by calculating ARL values. However, the significant extension of this paper over past works is in providing an effective first-order Markov model for dependent data based on individual binary observations.
ISBN:9781457706264
1457706261
DOI:10.1109/ICQR.2011.6031727