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Spatio-temporal prediction for log-Gaussian Cox processes.

Diggle, Peter J. and Brix, Anders (2001) Spatio-temporal prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society Series B (Statistical Methodology), 63 (4). pp. 823-841. ISSN 1467-9868

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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.

Item Type: Article
Journal or Publication Title: Journal of the Royal Statistical Society Series B (Statistical Methodology)
Additional Information: RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Subjects: Q Science > QA Mathematics
Departments: Faculty of Health and Medicine > Medicine
VC's Office
ID Code: 2412
Deposited By: ep_importer
Deposited On: 31 Mar 2008 14:16
Refereed?: Yes
Published?: Published
Last Modified: 06 Sep 2013 19:04
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2412

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