Abstract
Data in the form of time series of counts appear in a number of important applications such as health care services, financial markets, disease surveillance, and internet traffic modeling. One of the attractive models for this type of data is the so-called autoregressive conditional Poisson model (ACP). In this work, we propose a Stein-type shrinkage estimation strategy for the regression coefficients of the ACP model. The proposed estimators are expected to improve over the existing maximum partial likelihood estimators (MPLE) in terms of efficiency. We illustrate the usefulness of the proposed methods by using a data set on the daily number of patients at the Emergency Department (ED) of a hospital at eight o’clock in the morning, recorded over a period of 7 years.
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Acknowledgements
The authors would like to thank two anonymous referees for their constructive comments that helped improve the manuscript. The research of Dr. Ahmed and Dr. Hussein was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). Dr. Snowdon would like to acknowledge the funding from Mitacs and McKesson Canada Inc. which supported her research.
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Ahmed, S.E., Es-Sebaiy, K., Hussein, A., Ouassou, I., Snowdon, A. (2019). An Efficient Estimation Strategy in Autoregressive Conditional Poisson Model with Applications to Hospital Emergency Department Data. In: Ahmed, S., Carvalho, F., Puntanen, S. (eds) Matrices, Statistics and Big Data. IWMS 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-17519-1_12
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