Extreme values geostatistics make it possible to model the asymptotic behaviors of random phenomena which depends on space or time parameters. In this paper, we propose new models of the extremal coefficient within a spatial stationary fields underlied by multivariate copulas. Some models of extensions of the extremogram and the cross-extremogram are constructed in a spatial framework. Moreover, both these two geostatistcal tools are modeled using the extremal variogram which characterizes the asymptotic stochastic behavior of the phenomena.Comment: 16 pages, 3 figure
We introduce the extremal range, a local statistic for studying the spatial extent of extreme events...
Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Prefer...
A problem with use of the geostatistical Kriging error for optimal sampling design is that the desig...
AbstractStudying phenomena that follow a skewed distribution and entail an extremal behaviour is imp...
Conventional geostatistical methodology solves the problem of predicting the realized value of a lin...
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these prop...
There are many approaches to geostatistical simulation that can be used to generate realizations of ...
Conventional geostatistical methodology solves the problem of predicting the realised value of a lin...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
The areal modeling of the extremes of a natural process such as rainfall or temperature is important...
Preferential sampling refers to any situation in which the spatial process and the sampling location...
Geostatistics is extended to the spatial analysis of vector variables by defining the estimation var...
Discrete systems often manifest themselves as spatially continuous phenomena over a region of space....
International audienceHazard assessment at a regional scale may be performed thanks to a spatial mod...
Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`...
We introduce the extremal range, a local statistic for studying the spatial extent of extreme events...
Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Prefer...
A problem with use of the geostatistical Kriging error for optimal sampling design is that the desig...
AbstractStudying phenomena that follow a skewed distribution and entail an extremal behaviour is imp...
Conventional geostatistical methodology solves the problem of predicting the realized value of a lin...
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these prop...
There are many approaches to geostatistical simulation that can be used to generate realizations of ...
Conventional geostatistical methodology solves the problem of predicting the realised value of a lin...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
The areal modeling of the extremes of a natural process such as rainfall or temperature is important...
Preferential sampling refers to any situation in which the spatial process and the sampling location...
Geostatistics is extended to the spatial analysis of vector variables by defining the estimation var...
Discrete systems often manifest themselves as spatially continuous phenomena over a region of space....
International audienceHazard assessment at a regional scale may be performed thanks to a spatial mod...
Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`...
We introduce the extremal range, a local statistic for studying the spatial extent of extreme events...
Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Prefer...
A problem with use of the geostatistical Kriging error for optimal sampling design is that the desig...