To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical models, 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 or show some degree of skewness. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are handled simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson and Gamma, variograms and cross-variograms have convenient simple forms, and estimation of the parameters of the model can be carried out by Monte Carlo EM. This work has been inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).

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

MINOZZO, Marco;FERRARI, Clarissa
2013

Abstract

To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical models, 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 or show some degree of skewness. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are handled simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson and Gamma, variograms and cross-variograms have convenient simple forms, and estimation of the parameters of the model can be carried out by Monte Carlo EM. This work has been inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).
Generalized linear mixed model; Linear model of coregionalization; Markov chain Monte Carlo; Monte Carlo EM; Spatial factor model
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/474180
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