Spatiotemporal prediction for log-Gaussian Cox processes

• 成果类型: Article
• 署名作者: Brix, A; Diggle, PJ
• 署名单位: Lancaster University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00315
• 发表日期: 2001
• 页码: 823-841
• 关键词: time

摘要

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.