Indicator kriging (IK) is a spatial interpolation technique aimed at estimating the conditional cumulative distribution function (ccdf) of a variable at an unsampled location. Obtained results form a discrete approximation to this ccdf, and its corresponding discrete probability density function (cpdf) should be a vector, where each component gives the probability of an occurrence of a class. Therefore, this vector must have positive components summing up to one, like in a composition in the simplex. This suggests a simplicial approach to IK, based on the algebraic-geometric structure of this sample space: simplicial IK actually works with log-odds. Interpolated log-odds can afterwards be easily re-expressed as the desired cpdf or ccdf. An ...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
Kriging is an interpolation technique whose optimality criteria are based on normality assumptions e...
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The krig...
Indicator kriging (IK) is extended to analyze three-dimensional random unit vectors and evaluate the...
Prepared with the support of the National Science Foundation grant no. CME-7919836The theory of intr...
This report deals with Kriging, a spatial interpolation-method that enables making predictions of th...
Copyright © 2004 Elsevier LtdA probabilistic solution to the problem of spatial interpolation of a v...
The indicator approach to estimating spatial, local cumulative distributions is a well-known, non-pa...
Copyright © 2004 Elsevier LtdA probabilistic solution to the problem of spatial interpolation of a v...
Indicator Kriging (IK) was introduced by Journel in 1983, and since that time has grown to become on...
International audienceKriging is a special type of optimal linear prediction applied to random funct...
We consider the problem of spatial interpolation and outline the theory behind kriging and more spec...
In many applications, surprising high values designated as outliers are always faced, such as minera...
In this survey we present various classical geostatistical prediction methods with a focus on interp...
The objective of this paper is to examine the applicability of two geostatistical approaches, ordina...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
Kriging is an interpolation technique whose optimality criteria are based on normality assumptions e...
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The krig...
Indicator kriging (IK) is extended to analyze three-dimensional random unit vectors and evaluate the...
Prepared with the support of the National Science Foundation grant no. CME-7919836The theory of intr...
This report deals with Kriging, a spatial interpolation-method that enables making predictions of th...
Copyright © 2004 Elsevier LtdA probabilistic solution to the problem of spatial interpolation of a v...
The indicator approach to estimating spatial, local cumulative distributions is a well-known, non-pa...
Copyright © 2004 Elsevier LtdA probabilistic solution to the problem of spatial interpolation of a v...
Indicator Kriging (IK) was introduced by Journel in 1983, and since that time has grown to become on...
International audienceKriging is a special type of optimal linear prediction applied to random funct...
We consider the problem of spatial interpolation and outline the theory behind kriging and more spec...
In many applications, surprising high values designated as outliers are always faced, such as minera...
In this survey we present various classical geostatistical prediction methods with a focus on interp...
The objective of this paper is to examine the applicability of two geostatistical approaches, ordina...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
Kriging is an interpolation technique whose optimality criteria are based on normality assumptions e...
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The krig...