Spatiotemporal prediction for log‐Gaussian Cox processes

Anders Brix and Peter J. Diggle

Journal of the Royal Statistical Society Series B, 2001, vol. 63, issue 4, 823-841

Abstract: 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.

Date: 2001
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