Modern Statistics for Spatial Point Processes*


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    This paper was presented at the 21st Nordic Conference on Mathematical Statistics, Rebild, Denmark, June 2006 (NordStat 2006).

Jesper Møller, Department of Mathematical Sciences, Aalborg University.


Abstract.  We summarize and discuss the current state of spatial point process theory and directions for future research, making an analogy with generalized linear models and random effect models, and illustrating the theory with various examples of applications. In particular, we consider Poisson, Gibbs and Cox process models, diagnostic tools and model checking, Markov chain Monte Carlo algorithms, computational methods for likelihood-based inference, and quick non-likelihood approaches to inference.