Agenda

Moreno Bevilacqua, University of Valparaiso, Chile

Abstract:
We propose a new model for regression and dependence analysis when addressing spatial or spatiotemporal data with possibly heavy tails and an asymmetric marginal distribution. We first propose a stationary process with t marginals obtained through scale mixing of a Gaussian process with an inverse square root process with Gamma marginals. We then generalize this construction by considering a skew-Gaussian process, thus obtaining a process with skew-t marginal distributions.
For the proposed (skew) t process we study the second-order and geometrical properties and in the t case, we provide analytic expressions for the bivariate distribution. In an extensive simulation study, we investigate the use of the weighted pairwise likelihood as a method of estimation for the t process. Moreover we compare the performance of the optimal linear predictor of the t process versus the optimal Gaussian predictor. Finally, the effectiveness of our methodology is illustrated by analyzing a georeferenced dataset on maximum temperatures in Australia.

Bio Sketch:
Moreno Bevilacqua is Full Professor at the Statistics Department of  University of Valparaiso. His main research interests concern theory, methodology and applications in multivariate spatio-temporal statistics.