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Effective Control Charts for Monitoring the NGINAR(1) Process
Author(s) -
Li Cong,
Wang Dehui,
Zhu Fukang
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.1799
Subject(s) - overdispersion , control chart , count data , autocorrelation , statistics , autoregressive model , statistical process control , poisson distribution , chart , quasi likelihood , shewhart individuals control chart , mathematics , variance (accounting) , control limits , poisson regression , ewma chart , process (computing) , computer science , econometrics , accounting , business , operating system , population , demography , sociology
In recent years, there has been a growing interest in the control of autocorrelated count data. Existing results focus on the Poisson integer‐valued autoregressive (INAR) process, but this process cannot deal with overdispersion (variance is greater than mean), which is a common phenomenon in count data. We propose to control the autocorrelated count data based on a new geometric INAR (NGINAR) process, which is an alternative to the Poisson one. In this paper, we use the combined jumps chart, the cumulative sum chart, and the combined exponentially weighted moving average chart to detect the shift of parameters in the process. We compare the performance of these charts for the case of an underlying NGINAR(1) process in terms of the average run lengths. One real example is presented to demonstrate good performances of the charts. Copyright © 2015 John Wiley & Sons, Ltd.