Abstract: We present a framework for non-Gaussian spatial processes that encompasses large distribution families. Spatial dependence for a set of irregularly scattered locations is described with a mixture of pairwise kernels. Focusing on the nearest neighbors of a given location, within a reference set, we obtain a valid spatial process: the nearest neighbor mixture process (NNMP). We develop conditions to construct general NNMP models with arbitrary pre-specified marginal distributions. Essentially, NNMPs are specified by a bi-variate distribution, with suitable marginals, used to specify the mixture transition kernels. Such distribution can be spatially varying, to capture non-homogeneous spatial features. The mixture structure of the model allows for efficient MCMC-based exploration of posterior distribution of the model parameters, even for relatively large number of locations. We illustrate the capabilities of NNMPs with observations corresponding to distributions with different non-Gaussian characteristics: Long tails; Compact support; Skewness; Discrete values.

Bio: Dr. Sanso is a Professor of Statistics in the Department of Statistics of the University of California Santa Cruz, where he has been part of the faculty since the Fall of 2001 He obtained his PhD in mathematics at Universidad Central de Venezuela in 1992 with the dissertation Near Ignorance Classes for Bayesian Analysis written under the supervision of Luis Raúl Pericchi. After obtaining his PhD, his research activity focused on problems on robust Bayesian inference, and subsequently included work in problems related to model selection, meta-analysis and spatio-temporal modelling for rainfall and other environmental variables, always from a Bayesian viewpoint. Currently his work is focused on Bayesian spatio-temporal modeling, environmental and geostatistical applications, uncertainty quantification on complex computer models, statistical models for extreme values and statistical assessment of climate variability.