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Estimation Methods for a Flexible INAR(1) COM-Poisson Time Series Model


Time series of counts occur in many real-life situations where they exhibit various forms of dispersion. To facilitate the modeling of such time series, this paper introduces a flexible first-order integer-valued non-stationary autoregressive (INAR(1)) process where the innovation terms follow a Conway-Maxwell Poisson distribution (COM-Poisson). To estimate the unknown parameters in this model, different estimation approaches based on likelihood and quasi-likelihood formulations are considered. From simulation experiments and a real-life data application, the Generalized Quasi-Likelihood (GQL) approach yields estimates with lower bias than the other estimation approaches.

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Some applications of weighing designs

References Banerjee K.S. (1975): Weighing Designs for Chemistry, Medicine. Economics, Operations Research, Statistics. Marcel Dekker Inc., New York. Banerjee T., Mukerjee R. (2008): Optimal factorial designs for cDNA microarray experiments. Ann. Appl. Statist. 2: 366-385. Beckman R.J. (1973). An application of multivariate weighing designs. Communication in Statistics 1(6): 561-565. Box G.E., Hunter J.S., Hunter W.G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery

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