Project Details
Description
Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable for
statistical analysis, using spatio-temporal versions of intensity and inhomogeneous K-functions, quick
estimation procedures based on composite likelihoods and minimum contrast estimation, and easy simulation techniques.
These advantages are demonstrated in connection to the analysis of a relatively large dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent.
statistical analysis, using spatio-temporal versions of intensity and inhomogeneous K-functions, quick
estimation procedures based on composite likelihoods and minimum contrast estimation, and easy simulation techniques.
These advantages are demonstrated in connection to the analysis of a relatively large dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent.
Status | Finished |
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Effective start/end date | 01/09/2008 → 01/06/2011 |
Collaborative partners
- IIMAS, Universitdad Nacional Autónoma de Mexico (Project partner)
Funding
- <ingen navn>
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