Likelihood inference in multivariate model-based geostatistics

Multivariate model-based geostatistics refers to the extension of classical multivariate geostatistical techniques, in particular the classical linear model of coregionalization, to the case of non-Gaussian data. Extensions of this kind are still limited in the statistical literature, mainly for the inferential problems they pose, and almost invariably inference is carried out in a Bayesian context. In this work we present some new results on likelihood inference for the unknown parameters of a hierarchical geostatistical factor model. In particular, we show the implementation of some Monte Carlo EM algorithms and discuss their performances, in particular their sampling distributions, mainly through some simulation studies.