Robust principal component and factor analysis in the geostatistical treatment of environmental data

In this paper we show the usage of robust multivariate statistical methods in geostatistics. A usual procedure to estimate the values of variables (e.g. geochemical variables) measured at certain points of a region is to apply geostatistical methods like Krige estimation (based on the estimation of variograms). Here we emphasize robust principal component and factor analysis for the preliminary investigation of the data to reduce the dimension. Geostatistical methods are applied afterwards to the estimated factor scores. The final results show the influence of certain combinations of variables in the considered region. Moreover, the estimated factor scores with the robust procedure indicate outlying observations in a much better way. Copyright © 1999 John Wiley & Sons, Ltd.