ABSTRACT

In geostatistics it is common practice to assume that the underlying spatial process is stationary and isotropic, that is the spatial distribution is unchanged when the origin of the index set is translated and the process is stationary under rotations about the origin. However in environmental problems, it is not very realistic to make such assumptions since local influences in the correlation structure of the
spatial process may be clearly found in the data. On the other hand, when dealing with multivariate spatial processes, a critical specification is its cross covariance function. It is common practice to assume it is separable, which might not be a reasonable assumption. In this talk I will describe some models which propose flexible modelling of the covariance structure for univariate and multivariate spatial processes.