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Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy): spatial special issue

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  • Marco Minozzo
  • Clarissa Ferrari

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 was inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy). Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Marco Minozzo & Clarissa Ferrari, 2013. "Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy): spatial special issue," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 195-213, April.
    • Handle: RePEc:spr:alstar:v:97:y:2013:i:2:p:195-213
    DOI: 10.1007/s10182-012-0201-x
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    References listed on IDEAS

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    1. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    2. Hao Zhang, 2002. "On Estimation and Prediction for Spatial Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(1), pages 129-136, March.
    3. J. Zhu & J. C. Eickhoff & P. Yan, 2005. "Generalized Linear Latent Variable Models for Repeated Measures of Spatially Correlated Multivariate Data," Biometrics, The International Biometric Society, vol. 61(3), pages 674-683, September.
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    Cited by:

    1. Alessandro Fassò & Alessio Pollice & Barbara Cafarelli, 2013. "Spatial statistics for environmental studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 89-91, April.
    2. Marco Minozzo & Clarissa Ferrari, 2012. "Monte Carlo likelihood inference in multivariate model-based geostatistics," Working Papers 33/2012, University of Verona, Department of Economics.

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