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

Author

Listed:
  • Marco Minozzo

    () (Department of Economics (University of Verona))

  • Clarissa Ferrari

    () (Department of Economics (University of Verona))

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 di erent 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)

Suggested Citation

  • Marco Minozzo & Clarissa Ferrari, 2011. "Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy)," Working Papers 21/2011, University of Verona, Department of Economics.
  • Handle: RePEc:ver:wpaper:21/2011
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    File URL: http://dse.univr.it/home/workingpapers/MF-AStA-2011-09-02-submitted.pdf
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    References listed on IDEAS

    as
    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. Ole F. Christensen & Rasmus Waagepetersen, 2002. "Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(2), pages 280-286, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Generalized linear mixed model; linear model of coregionalization; Markov chain Monte Carlo; Monte Carlo EM; spatial factor model;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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