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 1369-7412

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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:
Journal 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
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
2412
Deposited By:
Deposited On:
31 Mar 2008 13:16
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 12:16