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.
Original language | English (US) |
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Pages (from-to) | 823-841 |
Number of pages | 19 |
Journal | Journal of the Royal Statistical Society. Series B: Statistical Methodology |
Volume | 63 |
Issue number | 4 |
State | Published - 2001 |
Externally published | Yes |
Keywords
- Markov process
- Metropolis adjusted Langevin algorithm
- Ornstein-Uhlenbeck process
- Space-time point process
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability