Spatiotemporal prediction for log-Gaussian Cox processes

Anders Brix; Peter Diggle

doi:10.1111/1467-9868.00315

Spatiotemporal prediction for log-Gaussian Cox processes

Anders Brix
, Peter Diggle

Lancaster University

Research output: Contribution to journal › Article › peer-review

153 Citations (Scopus)

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
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
DOIs	https://doi.org/10.1111/1467-9868.00315
Publication status	Published - 1 Jan 2001
Externally published	Yes

Keywords

Markov process
Metropolis adjusted Langevin algorithm
Ornstein-Uhlenbeck process
Space-time point process

Access to Document

10.1111/1467-9868.00315

Corrigendum: Spatiotemporal prediction for log-Gaussian Cox processes [J. R. Statist. Soc. B 63 (2001) 823-841]: Spatiotemporal prediction for log-Gaussian Cox processes [J. R. Statist. Soc. B 63 (2001) 823-841]
Taylor, B. M. & Diggle, P., 1 Jun 2013, 2 p.
Research output: Other contribution

Open Access

3 Citations (Scopus)
Erratum: Spatiotemporal prediction for log-Gaussian Cox processes
Brix, A. & Diggle, P., 1 Jan 2003, 1 p.
Research output: Other contribution

Open Access

7 Citations (Scopus)

Cite this

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title = "Spatiotemporal prediction for log-Gaussian Cox processes",

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.",

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AU - Diggle, Peter

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AB - 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.

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KW - Metropolis adjusted Langevin algorithm

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KW - Space-time point process

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JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology

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ER -

Spatiotemporal prediction for log-Gaussian Cox processes

Abstract

Keywords

Access to Document

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Research output

Corrigendum: Spatiotemporal prediction for log-Gaussian Cox processes [J. R. Statist. Soc. B 63 (2001) 823-841]: Spatiotemporal prediction for log-Gaussian Cox processes [J. R. Statist. Soc. B 63 (2001) 823-841]

Erratum: Spatiotemporal prediction for log-Gaussian Cox processes

Cite this