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

  1. Jakob G. Rasmussen1,
  2. Jesper Møller1,
  3. Brian H. Aukema2,
  4. Kenneth F. Raffa3,
  5. Jun Zhu3

Article first published online: 7 AUG 2007

DOI: 10.1111/j.1467-9868.2007.00608.x

Issue

Journal of the Royal Statistical Society: Series B (Statistical Methodology)

Journal of the Royal Statistical Society: Series B (Statistical Methodology)

Volume 69, Issue 4, pages 701–713, September 2007

Additional Information

How to Cite

Rasmussen, J. G., Møller, J., Aukema, B. H., Raffa, K. F. and Zhu, J. (2007), Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69: 701–713. doi: 10.1111/j.1467-9868.2007.00608.x

Author Information

  1. 1

    Aalborg University, Denmark

  2. 2

    Canadian Forest Service, Prince George, and University of Northern British Columbia, Prince George, Canada

  3. 3

    University of Wisconsin—Madison, USA

*Jesper Møller, Department of Mathematical Sciences, Aalborg University, Fredrik Bajers Vej 7 G, 9220 Aalborg Øst, Denmark. E-mail: jm@math.aau.dk

Publication History

  1. Issue published online: 7 AUG 2007
  2. Article first published online: 7 AUG 2007
  3. [Received August 2006. Revised April 2007]

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Keywords:

  • Bark-beetle;
  • Bayesian inference;
  • Forest entomology;
  • Markov chain Monte Carlo methods;
  • Missing data;
  • Multivariate point process;
  • Prediction;
  • Spatial–temporal process

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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