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

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  • Jakob G. Rasmussen
  • Jesper Møller
  • Brian H. Aukema
  • Kenneth F. Raffa
  • Jun Zhu

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.

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  • Jakob G. Rasmussen & Jesper Møller & Brian H. Aukema & Kenneth F. Raffa & Jun Zhu, 2007. "Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 701-713, September.
  • Handle: RePEc:bla:jorssb:v:69:y:2007:i:4:p:701-713
    DOI: 10.1111/j.1467-9868.2007.00608.x
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    Cited by:

    1. Jesper Møller & Rasmus P. Waagepetersen, 2007. "Modern Statistics for Spatial Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 643-684, December.

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