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|Authors:||Mark D. Risser, Catherine A. Calder|
|Title:||Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R|
|Abstract:||In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covariance function for spatial Gaussian process models that allows for efficient computing in two ways: first, by representing the spatially-varying parameters via a discrete mixture or "mixture component" model, and second, by estimating the mixture component parameters through a local likelihood approach. In order to make computations for a convolutionbased nonstationary spatial model readily available, this paper also presents and describes the convoSPAT package for R. The nonstationary model is fit to both a synthetic data set and a real data application involving annual precipitation to demonstrate the capabilities of the package.|
Page views:: 1388. Submitted: 2015-07-28. Published: 2017-11-13.
Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R
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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.