We propose a new model for regression and depen-dence analysis when addressing spatial data with pos-sibly heavy tails and an asymmetric marginal distri-bution. We first propose a stationary process withtmarginals obtained through scale mixing of a Gaus-sian process with an inverse square root process withGammamarginals.Wethengeneralizethisconstructionby considering a skew-Gaussian process, thus obtain-ing a process with skew-t marginal distributions. For theproposed (skew)tprocess, we study the second-orderand geometrical properties and in thetcase, we provideanalytic expressions for the bivariate distribution. In anextensive simulation study, we investigate the use of theweighted pairwise likelihood as a method of estimationfor thetprocess.Moreover we compare the performanceof the optimal linear predictor of thetprocess versus theoptimal Gaussian predictor. Finally, the effectiveness ofour methodology is illustrated by analyzing a georefer-enced dataset on maximum temperatures in Australia<\/p>\n","protected":false},"excerpt":{"rendered":"We propose a new model for regression and depen-dence analysis when addressing spatial data with pos-sibly heavy tails and an…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[85],"tags":[],"class_list":["post-8384","post","type-post","status-publish","format-standard","hentry","category-articulo"],"acf":[],"yoast_head":"\n