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Spatiotemporal prediction for log‐Gaussian Cox processes
Author(s) -
Brix Anders,
Diggle Peter J.
Publication year - 2001
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00315
Subject(s) - cox process , point process , markov chain monte carlo , moment (physics) , computer science , gaussian process , markov chain , set (abstract data type) , statistical physics , point (geometry) , monte carlo method , markov process , stochastic process , gaussian , data mining , mathematics , statistics , machine learning , poisson distribution , poisson process , physics , programming language , geometry , classical mechanics , quantum mechanics
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.