TY - JOUR
T1 - A General approach for monitoring serially dependent categorical processes
AU - Li, Jian
AU - Zhou, Qiang
N1 - Funding Information:
The authors would like to thank the editor and two anonymous referees for their insightful comments and constructive suggestions that have resulted in significant improvements in this article. Dr. Li’s research was supported by the National Natural Science Foundation of China Grants 71402133, 71602155, 71572138, and 11501209, and the Open
Funding Information:
Fund of State Key Laboratory for Manufacturing Systems Engineering (Xi’an Jiaotong University) sklms2016010. Dr. Zhou’s research was supported by GRF Grant 11272216 from Hong Kong Research Grants Council.
PY - 2017/10
Y1 - 2017/10
N2 - We consider the statistical surveillance for serially dependent categorical processes, where observations exhibit temporal dependence and have several attribute levels. In the literature, relevant methods focus on serially dependent binary data with two attribute levels and are mainly constructed from a first-order Markov chain. However, they cannot be applied to multinary data with three or more attribute levels. In addition, a Markov chain seems not to be a good choice because it cannot characterize the joint dynamics among the current observation and its past values. In this article, we adopt a multivariate categorical setting of the data and develop a general approach for monitoring serially dependent categorical processes, from binary to multinary, and from first-order dependency to higher-order dependency. Simulation results have demonstrated its robustness to various shifts in marginal probabilities and dependence structure, including autocorrelation coefficients and dependence order.
AB - We consider the statistical surveillance for serially dependent categorical processes, where observations exhibit temporal dependence and have several attribute levels. In the literature, relevant methods focus on serially dependent binary data with two attribute levels and are mainly constructed from a first-order Markov chain. However, they cannot be applied to multinary data with three or more attribute levels. In addition, a Markov chain seems not to be a good choice because it cannot characterize the joint dynamics among the current observation and its past values. In this article, we adopt a multivariate categorical setting of the data and develop a general approach for monitoring serially dependent categorical processes, from binary to multinary, and from first-order dependency to higher-order dependency. Simulation results have demonstrated its robustness to various shifts in marginal probabilities and dependence structure, including autocorrelation coefficients and dependence order.
KW - Autocorrelation Coefficient
KW - Conditional Probability
KW - Contingency Table
KW - Log-Linear Model
KW - Statistical Process Control
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U2 - 10.1080/00224065.2017.11918003
DO - 10.1080/00224065.2017.11918003
M3 - Article
AN - SCOPUS:85031116936
SN - 0022-4065
VL - 49
SP - 365
EP - 379
JO - Journal of Quality Technology
JF - Journal of Quality Technology
IS - 4
ER -