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http://dx.doi.org/10.5351/KJAS.2003.16.1.071

A Markov Chain Representation of Statistical Process Monitoring Procedure under an ARIMA(0,1,1) Model  

박창순 (중앙대학교 수학통계학부)
Publication Information
The Korean Journal of Applied Statistics / v.16, no.1, 2003 , pp. 71-85 More about this Journal
Abstract
In the economic design of the process control procedure, where quality is measured at certain time intervals, its properties are difficult to derive due to the discreteness of the measurement intervals. In this paper a Markov chain representation of the process monitoring procedure is developed and used to derive its properties when the process follows an ARIMA(0,1,1) model, which is designed to describe the effect of the noise and the special cause in the process cycle. The properties of the Markov chain depend on the transition matrix, which is determined by the control procedure and the process distribution. The derived representation of the Markov chain can be adapted to most different types of control procedures and different kinds of process distributions by obtaining the corresponding transition matrix.
Keywords
ARIMA(0,1,1); process monitoring; Markov chain; ARIMA(0,1,1); Transition matrix; Renewal process;
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