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 1369-7412Full text not available from this repository.
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.
|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|
|Deposited On:||31 Mar 2008 14:16|
|Last Modified:||07 Jan 2015 13:52|
Actions (login required)