Non‐Gaussian geostatistical modeling using (skew) t processes

Moreno Bevilacqua, Christian Caamaño‐Carrillo, Reinaldo B. Arellano‐Valle and Víctor Morales‐Oñate
Authors registered in the RePEc Author Service: Víctor Roberto Morales-Oñate

Scandinavian Journal of Statistics, 2021, vol. 48, issue 1, 212-245

Abstract: We propose a new model for regression and dependence analysis when addressing spatial 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.

Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:48:y:2021:i:1:p:212-245

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