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