Projektdetaljer
Beskrivelse
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 | Afsluttet |
---|---|
Effektiv start/slut dato | 01/09/2008 → 01/06/2011 |
Samarbejdspartnere
- IIMAS, Universitdad Nacional Autónoma de Mexico (Projektpartner)
Finansiering
- <ingen navn>
Fingerprint
Udforsk forskningsemnerne, som dette projekt berører. Disse etiketter er oprettet på grundlag af de underliggende bevillinger/legater. Sammen danner de et unikt fingerprint.