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Article

Monitoring Parameter Change for Time Series Models of Counts Based on Minimum Density Power Divergence Estimator

Department of Statistics, Seoul National University, Seoul 08826, Korea
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Entropy 2020, 22(11), 1304; https://doi.org/10.3390/e22111304
Received: 28 October 2020 / Revised: 11 November 2020 / Accepted: 14 November 2020 / Published: 16 November 2020
In this study, we consider an online monitoring procedure to detect a parameter change for integer-valued generalized autoregressive heteroscedastic (INGARCH) models whose conditional density of present observations over past information follows one parameter exponential family distributions. For this purpose, we use the cumulative sum (CUSUM) of score functions deduced from the objective functions, constructed for the minimum power divergence estimator (MDPDE) that includes the maximum likelihood estimator (MLE), to diminish the influence of outliers. It is well-known that compared to the MLE, the MDPDE is robust against outliers with little loss of efficiency. This robustness property is properly inherited by the proposed monitoring procedure. A simulation study and real data analysis are conducted to affirm the validity of our method. View Full-Text
Keywords: time series of counts; INGARCH model; SPC; CUSUM monitoring; MDPDE time series of counts; INGARCH model; SPC; CUSUM monitoring; MDPDE
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MDPI and ACS Style

Lee, S.; Kim, D. Monitoring Parameter Change for Time Series Models of Counts Based on Minimum Density Power Divergence Estimator. Entropy 2020, 22, 1304. https://doi.org/10.3390/e22111304

AMA Style

Lee S, Kim D. Monitoring Parameter Change for Time Series Models of Counts Based on Minimum Density Power Divergence Estimator. Entropy. 2020; 22(11):1304. https://doi.org/10.3390/e22111304

Chicago/Turabian Style

Lee, Sangyeol, and Dongwon Kim. 2020. "Monitoring Parameter Change for Time Series Models of Counts Based on Minimum Density Power Divergence Estimator" Entropy 22, no. 11: 1304. https://doi.org/10.3390/e22111304

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