Support vector regression builds a model of a process that depends on a set of factors. It traditionally considers one output at a time, which means that advantage cannot be taken of the correlations that may exist between outputs. The purpose of this paper is to show how the body of knowledge accumulated by geostatisticians on Kriging and its extensions over the last 40 years can help extend s...

For many practical problems in environmental management, information about soil heavy metals, relative to threshold values that may be of practical importance is needed at unsampled sites. The Hangzhou-Jiaxing-Huzhou (HJH) Plain has always been one of the most important rice production areas in Zhejiang province, China, and the soil heavy metal concentration is directly related to the crop qual...

Until now, Universal Kriging has not been used for the mapping of geological data in Croatia. However, it is one of the most frequently used methods of Kriging, probably the most adequate in cases when the input data is marked by a common trend. That exact feature is often an attribute of deep geological data, and thereby that of structural maps. Mapped surfaces in a row of examples have a stru...

The paper contains a combination of two approaches generalising the usual kriging technique for prediction in elds: the Bayesian approach incorporating prior knowledge on the eld and the fuzzy set approach re ecting uncertainty w.r.t. observation impreciseness and speci cation vagueness. The presentation includes a numerical example. c © 2000 Elsevier Science B.V. All rights reserved.

We present statistics (S-statistics) based only on random variable (not random value) with a mean squared error of mean estimation as a concept of error.

We consider the problem of constructing metamodels for computationally expensive simulation codes; that is, we construct interpolation/prediction of functions values (responses) from a finite collection of evaluations (observations). We use Gaussian process modeling and Kriging, and combine a Bayesian approach, based on a finite set of covariance functions, with the use of localized models, ind...

A linear geostatistical model is considered. Properties of a universal kriging are studied when the locations of observations are measured with errors. Alternative prediction procedures are introduced and their least squares errors are analyzed.