Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

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Abstract

Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared with discrete time processes in the setting of the present paper as well as other spatial-temporal situations.
Original languageEnglish
JournalJournal of The Royal Statistical Society Series B-statistical Methodology
Volume69
Issue number4
Pages (from-to)701-713
Number of pages13
ISSN1369-7412
DOIs
Publication statusPublished - 2007

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Continuous Time
Modeling
Point Process
Normalizing Constant
Discrete-time Model
Bayesian inference
Specificity
Discrete-time
Continuous time
Model-based
Unknown
Interaction
Simulation
Point process

Cite this

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title = "Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times",
abstract = "Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared with discrete time processes in the setting of the present paper as well as other spatial-temporal situations.",
author = "Rasmussen, {Jakob Gulddahl} and Jesper M{\o}ller",
year = "2007",
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journal = "Journal of the Royal Statistical Society, Series B (Statistical Methodology)",
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AU - Rasmussen, Jakob Gulddahl

AU - Møller, Jesper

PY - 2007

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N2 - Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared with discrete time processes in the setting of the present paper as well as other spatial-temporal situations.

AB - Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared with discrete time processes in the setting of the present paper as well as other spatial-temporal situations.

U2 - 10.1111/j.1467-9868.2007.00608.x

DO - 10.1111/j.1467-9868.2007.00608.x

M3 - Journal article

VL - 69

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EP - 713

JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

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