ICM 1998, Berlin, Aug. 18–27

Abstracts of Short Communications and Posters

Section:

12. Probability and Statistics

1991 MS Classification: 62, 92, 60 Pawlowsky-Glahn, Vera, Universitat Politècnica de Catalunya, Spain; Egozcue-Rubí, Juan-José, Universitat Politècnica de Catalunya, Spain; Heinz Burger, Freie Universität Berlin, Germany: Distances for multivariate spatial data analysis. Many techniques of multivariate statistical analysis require a distance between sampled vectors, but none takes into account the case in which samples are obtained from a realization of a spatial correlated random field. Examples of this are found in frameworks such as geostatistics or epidemiology in which individuals are characterized by a random vector plus non-random spatial coordinates. A model, defined as the sum of a spatial autocorrelated random field, white noise and a random vector whose spatial autocovariance matrix is constant for any lag, and its associated distances, is introduced as a first step towards the development of appropriate statistical techniques for the analysis of multivariate spatial data.