To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical methods, are increasingly being used. These methods, however, are optimal only in the case in which the data may be assumed Gaussian and do not properly cope with data measurements that are discrete, nonnegative, show some degree of skewness, or present exact zeroes. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are allowed to be dealt with simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson, Gamma and Compound Gamma, variograms and cross-variograms have convenient simple forms. This work has been inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).

Multivariate geostatistical mapping of radioactive contamination in the Maddalena Arcipelago (Sardinia, Italy)

MINOZZO, Marco;PIERRI, Francesca
2005-01-01

Abstract

To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical methods, are increasingly being used. These methods, however, are optimal only in the case in which the data may be assumed Gaussian and do not properly cope with data measurements that are discrete, nonnegative, show some degree of skewness, or present exact zeroes. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are allowed to be dealt with simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson, Gamma and Compound Gamma, variograms and cross-variograms have convenient simple forms. This work has been inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/153124
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