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Modern Statistics for Spatial Point Processes *
Author(s) -
MØLLER JESPER,
WAAGEPETERSEN RASMUS P.
Publication year - 2007
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2007.00569.x
Subject(s) - point process , mathematics , markov chain monte carlo , inference , cox process , gibbs sampling , markov chain , computational statistics , algorithm , monte carlo method , poisson distribution , econometrics , statistical physics , computer science , statistics , artificial intelligence , poisson process , bayesian probability , physics
. 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.