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Activity Number: 87
Type: Invited
Date/Time: Sunday, August 3, 2014 : 8:30 PM to 10:30 PM
Sponsor: ENAR
Abstract #311645
Title: Nonstationary Spatial Modeling via Covariance Regression
Author(s): Mark Risser*+ and Kate Calder
Companies: Ohio State University and Ohio State University
Keywords: nonstationarity ; Gaussian process ; spatial statistics ; covariance regression ; Bayesian
Abstract:

Much of the recent literature on Gaussian process models for spatial data focuses on nonstationary methods, but many of these more appropriate models suffer from difficulties in model fitting, parameter identification, and interpretability. To overcome these issues, we build on the growing literature of covariate-driven nonstationary spatial modeling. Using process convolution techniques, we propose a Bayesian model for continuously-indexed spatial data based on a flexible covariance regression structure for a convolution-kernel covariance matrix. Unlike current approaches, our approach constrains the components of the convolution-kernel matrix to vary smoothly over space according to spatially-varying covariate information. We explore the properties of the implied model, including a description of the implied nonstationary covariance function and the interpretational benefits in the kernel parameters, and demonstrate that our parsimonious model provides a compromise between stationary and overly parameterized nonstationary models that do not perform well in practice. We illustrate our approach through simulation and an analysis of precipitation data.


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