Generalized Poisson geometric process model

What is it about?

We analyze Cannabis offences count series which display substantial over- and under-dispersion, trend movement and population heterogeneity. We develop the generalized Poisson geometric process mixture model to capture these features and to classify homogeneous regions. The model is implemented using Markov chain Monte Carlo algorithm via the WinBUGS software and its performance is evaluated through a simulation study.

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Why is it important?

We develop the generalized Poisson geometric process mixture model adopting a state space and observation driven approaches. Current literature on count series modeling which can capture certain common features including over- and under-dispersion, trend movement and population heterogeneity is limited and this paper aims to fill in such gap.

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