Abstract
ABSTRACTThis paper derives models to analyse Cannabis offences count series from New South Wales, Australia. The data display substantial overdispersion as well as underdispersion for a subset, trend movement and population heterogeneity. To describe the trend dynamic in the data, the Poisson geometric process model is first adopted and is extended to the generalized Poisson geometric process model to capture both over- and underdispersion. By further incorporating mixture effect, the model accommodates population heterogeneity and enables classification of homogeneous units. The model is implemented using Markov chain Monte Carlo algorithms via the user-friendly WinBUGS software and its performance is evaluated through a simulation study.
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