Path:  Home   >  Nonparametric first-order analysis of spatial and spatio-temporal point processes. Application to wildfire patterns
 
Nonparametric first-order analysis of spatial and spatio-temporal point processes. Application to wildfire patterns
 
 
Description:  Spatial point patterns arise in a wide variety of scientific contexts, including seismology, forestry, geography and epidemiology. Wildfire is the most ubiquitous natural disturbance in the world and represents a problem of considerable social and environmental importance; particularly, in Galicia (NW Spain) arson fires are the main cause of forest destruction. Knowing the spatial distribution of forest fires would be a key factor for future development of fire prevention and fire fighting plans. Nonparametric estimation and bootstrap techniques play an important role in many areas of Statistics. In the point process framework, kernel intensity estimation has been limited to exploratory analysis due to its lack of consistency. This work addresses different procedures to obtain a consistent estimator of the first order intensity such as kernel estimation of the density of event locations and kernel intensity estimation based on covariates. We propose a smooth bootstrap procedure for inhomogeneous point processes in order to develop effective bandwidth selectors for kernel intensity estimation. The consistent estimators introduced above, are used to estimate the first order intensity of the wildfires registered in Galicia during the period 1999-2008. Finally this kind of estimators is used for two problems of interest: a) The nonparametric comparison of first-order intensity functions and b) One separability test for spatio-temporal point process.
Date:  2018-01-19
Start Time:   11:30
Speaker:  Wenceslao González Manteiga (Univ. Santiago de Compostela, Spain)
Institution:  Univ. Santiago de Compostela, Spain
Place:  Room 5.5
Research Groups: -Probability and Statistics
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