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Spatio-temporal prediction for log-Gaussian Cox processes.

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Spatio-temporal prediction for log-Gaussian Cox processes. / Diggle, Peter J.; Brix, Anders.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, No. 4, 01.04.2001, p. 823-841.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Diggle, PJ & Brix, A 2001, 'Spatio-temporal prediction for log-Gaussian Cox processes.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 4, pp. 823-841. https://doi.org/10.1111/1467-9868.00315

APA

Diggle, P. J., & Brix, A. (2001). Spatio-temporal prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(4), 823-841. https://doi.org/10.1111/1467-9868.00315

Vancouver

Diggle PJ, Brix A. Spatio-temporal prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2001 Apr 1;63(4):823-841. doi: 10.1111/1467-9868.00315

Author

Diggle, Peter J. ; Brix, Anders. / Spatio-temporal prediction for log-Gaussian Cox processes. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2001 ; Vol. 63, No. 4. pp. 823-841.

Bibtex

@article{a5fcd5b39120421b828274a164f01581,
title = "Spatio-temporal 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.",
author = "Diggle, {Peter J.} and Anders Brix",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2001",
month = apr,
day = "1",
doi = "10.1111/1467-9868.00315",
language = "English",
volume = "63",
pages = "823--841",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Spatio-temporal prediction for log-Gaussian Cox processes.

AU - Diggle, Peter J.

AU - Brix, Anders

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2001/4/1

Y1 - 2001/4/1

N2 - 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.

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.

U2 - 10.1111/1467-9868.00315

DO - 10.1111/1467-9868.00315

M3 - Journal article

VL - 63

SP - 823

EP - 841

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

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

SN - 1369-7412

IS - 4

ER -