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Activity Number: 121 - Handling Large Dimensionality, Skewness and Non-Stationarity Through Multi-Resolution Spatial Modeling
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #303002
Title: A Bi-Resolution Spatial Model Based on the Skew-T Distribution
Author(s): Stefano Castruccio* and Felipe Tagle and Marc Genton
Companies: University of Notre Dame and University of Notre Dame and King Abdullah University of Science and Technology
Keywords: skew t; wind power; wind; non Gaussian

The Gaussian assumption underlies much of the stochastic process theory for spatially-referenced datasets due to its analytical tractability. Several approaches have been developed in recent decades to reconcile this theory with non-Gaussian features that are commonly found in observational datasets across a broad range of applications. The skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature. This article presents a spatial model based on the skew-t distribution that aids in modeling spatial configurations characterized by an interaction between a large-scale effect and region-specific fine-scale effects. The model's predictive ability is compared against another model in the literature, and illustrated using daily wind speeds over Saudi Arabia.

Authors who are presenting talks have a * after their name.

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