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    A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

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    1712.01992v1.pdf
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    Description:
    Preprint
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    Type
    Preprint
    Authors
    Tagle, Felipe
    Castruccio, Stefano
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2640
    Date
    2017-12-06
    Permanent link to this record
    http://hdl.handle.net/10754/626455
    
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    Abstract
    Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.
    Publisher
    arXiv
    arXiv
    arXiv:1712.01992
    Additional Links
    http://arxiv.org/abs/1712.01992v1
    http://arxiv.org/pdf/1712.01992v1
    Collections
    Preprints; Preprints; Statistics Program; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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