Kriging is a family of linear methods for the estimation of physical quantities with spatial dependence which are optimal in the squared minima sense. To perform the interpolation, kriging considers, in addition to the value and location of the observations, the spatial correlation of the quantity by means of variogram, the random fluctuations of the measured magnitude and the resolution of the measuring devices. The traditional way kriging equations are solved involves the resolution of inverse of great matrices, so that it is normally quite time consuming. Comparing the uncertainty obtained with kriging (for magnitudes with spatial dependence) with standard techniques for uncertainty estimation, we have seen that for the case of regular s...
International audienceKriging consists in estimating or predicting the spatial phenomenon at non sam...
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibi...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
In optical metrology the final experimental result is normally an image acquired with a CCD camera. ...
This report deals with Kriging, a spatial interpolation-method that enables making predictions of th...
AbstractIn this paper a Bayesian alternative to Kriging is developed. The latter is an important too...
International audienceGeostatistics is a branch of statistics dealing with spatial variability. Geos...
© 2015 Elsevier Ltd. All rights reserved. A Kriging regression model is developed as a post-processi...
International audienceGeostatistics is a branch of statistics dealing with spatial phenomena modelle...
Variograms are used to describe the spatial variability of environmental variables. In this study, t...
Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of ...
A datum is considered spatial if it contains locational information. Typically, there is also attrib...
Leading researchers in methods of Spatial Statistics advocate applying kriging methods to samples of...
This dissertation, comprising two distinct papers, investigates the prediction and sampling of spati...
Although linear kriging is a distribution-free spatial interpolator, its efficiency is maximal only ...
International audienceKriging consists in estimating or predicting the spatial phenomenon at non sam...
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibi...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
In optical metrology the final experimental result is normally an image acquired with a CCD camera. ...
This report deals with Kriging, a spatial interpolation-method that enables making predictions of th...
AbstractIn this paper a Bayesian alternative to Kriging is developed. The latter is an important too...
International audienceGeostatistics is a branch of statistics dealing with spatial variability. Geos...
© 2015 Elsevier Ltd. All rights reserved. A Kriging regression model is developed as a post-processi...
International audienceGeostatistics is a branch of statistics dealing with spatial phenomena modelle...
Variograms are used to describe the spatial variability of environmental variables. In this study, t...
Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of ...
A datum is considered spatial if it contains locational information. Typically, there is also attrib...
Leading researchers in methods of Spatial Statistics advocate applying kriging methods to samples of...
This dissertation, comprising two distinct papers, investigates the prediction and sampling of spati...
Although linear kriging is a distribution-free spatial interpolator, its efficiency is maximal only ...
International audienceKriging consists in estimating or predicting the spatial phenomenon at non sam...
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibi...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...