Mapping data using geostatistics can be a time-consuming process because of the many parameters to define. automap, a geostatistical package written in R, was developed to define automatically a spatial correlation model, a step that is considered to be the biggest obstacle for automating the spatial interpolation process with geostatistical algorithms. The implementation of automap into a Serv...

The methods of classical geostatistics were primarily developed under a set of constraining assumptions (linear estimator, measurements are exacts, etc.) and they lack the theoretical underpinnings and practical flexibility to incorporate the many sources of information available in modern days sciences (such as physical laws, empirical models, higher statistical moments, uncertain information)...

Geostatistics is a branch of statistics dealing with spatial phenomena modelled by random functions. In particular, it is assumed that, under some wellchosen simplifying hypotheses of stationarity, this probabilistic model, i.e. the random function describing spatial dependencies, can be completely assessed from the dataset by the experts. Kriging is a method for estimating or predicting the sp...

We apply different advantages of the optimal genetic searching, geostatistics, and fuzzy c-means clustering to the segmentation of gray-level images. The proposed method can deal effectively with noisy image segmentation.

Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Stre...

The trend in interactive entertainment is towards scenes with massive numbers of characters, and requiring huge amounts of motion data, which must be compactly and efficiently stored without sacrificing quality or controllability of the motions. Multilinear algebra is a powerful tool for efficiently representing multivariate data, including human motion data, through the analysis of multimodal ...

Two robust approaches to principal component analysis and factor analysis are presented. The different methods are compared, and properties are discussed. As an application we use a large geochemical data set which was analyzed in detail by univariate (geo-)statistical methods. We explain the advantages of applying robust multivariate methods.