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Activity Number: 488 - Nonstationary and Anisotropic Spatial Processes
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #330912 Presentation
Title: Examining Non-Stationarity in Spatial Processes via an M-RA and Mixture Priors
Author(s): Veronica J. Berrocal*
Companies: University of Michigan
Keywords: Multi-resolution approximation; non-stationary covariance function; spatial prediction; basis function expansion; mixture prior; Bayesian hierarchical model

Predicting a point-referenced spatial process at unobserved locations is one of the typical goals of a geostatistical analysis. If the spatial process is assumed to admit a covariance function, then to generate such predictions, it is necessary to know the form of the covariance function. In many instances, mostly because of computational convenience, researchers opt for a stationary covariance function, even though in reality the process might be non-stationary. In this paper, building upon the M-RA approach of Katzfuss (2017), we present a Bayesian hierarchical modeling framework that allows to handle both stationary data and globally non-stationary but locally stationary data, without the need to specify a priori a non-stationary covariance function. In both simulation experiments and a real data application, we show that our model, the mixture M-RA, obtained by embedding mixture priors within the M-RA framework, not only allows to detect regions of local stationarity but also outperforms other standard spatial modeling approaches.

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

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