Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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A latent variable approach to modelling multivariate geostatistical skew-normal data
Luca Bagnato, Marco Minozzo

Last modified: 2013-06-14

Abstract


In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew-normal data. In this model we assume that the un observed latent structure, responsible for the correlation among different variables as well as for the spatial autocorrelation among different sites is Gaussian, and that the observed variables are skew-normal. For this model we provide some of its properties like its spatial autocorrelation structure and its finite dimensional marginal distributions. Estimation of the unknown parameters of the model is carried out by employing a Monte Carlo Expectation Maximization algorithm, whereas prediction at unobserved sites is performed by using closed form formulas and Markov Chain Monte Carlo algorithms. Simulation studies have been performed to evaluate the soundness of the proposed procedures.


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