Modern Statistics for Spatial Point Processes

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

136 Citationer (Scopus)

Resumé

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.
Udgivelsesdato: DEC
OriginalsprogEngelsk
TidsskriftScandinavian Journal of Statistics
Vol/bind34
Udgave nummer4
Sider (fra-til)643-684
Antal sider42
ISSN0303-6898
DOI
StatusUdgivet - 2007

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Spatial Point Process
Cox Process
Statistics
Model Diagnostics
Cox Model
Markov Chain Monte Carlo Algorithms
Random Effects Model
Generalized Linear Model
Computational Methods
Process Model
Model Checking
Analogy
Likelihood
Siméon Denis Poisson
Inference
Point process
Model checking
Process theory
Cox process
Generalized linear model

Citer dette

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Modern Statistics for Spatial Point Processes. / Møller, Jesper; Waagepetersen, Rasmus.

I: Scandinavian Journal of Statistics, Bind 34, Nr. 4, 2007, s. 643-684.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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