Spatiotemporal prediction for log-Gaussian Cox processes

Anders Brix, Peter J. Diggle

Research output: Contribution to journalArticlepeer-review

110 Scopus citations


Space-time point pattern data have become more widely available as a result of technological developments in areas such as geographic information systems. We describe a flexible class of space-time point processes. Our models are Cox processes whose stochastic intensity is a space-time Ornstein-Uhlenbeck process. We develop moment-based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and illustrate the performance of our methods on a synthetic data set.

Original languageEnglish (US)
Pages (from-to)823-841
Number of pages19
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number4
StatePublished - 2001
Externally publishedYes


  • Markov process
  • Metropolis adjusted Langevin algorithm
  • Ornstein-Uhlenbeck process
  • Space-time point process

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability


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