Modern Statistics for Spatial Point Processes

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136 Citations (Scopus)

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.
Original languageEnglish
JournalScandinavian Journal of Statistics
Volume34
Issue number4
Pages (from-to)643-684
Number of pages42
ISSN0303-6898
DOIs
Publication statusPublished - 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

Cite this

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title = "Modern Statistics for Spatial Point Processes",
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.",
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}

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

In: Scandinavian Journal of Statistics, Vol. 34, No. 4, 2007, p. 643-684.

Research output: Contribution to journalJournal articleResearchpeer-review

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AB - 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.

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