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Activity Number: 195
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #308961
Title: A Nonstationary Spatial Covariance Regression Model
Author(s): Mark Risser*+ and Catherine A. Calder
Companies: The Ohio State University and Ohio State University
Keywords: Bayesian modeling ; spatial statistics ; environmental statistics

Many of the recent developments in spatial statistics revolve around alternative covariance models to more accurately reflect the non-stationary realities of spatial processes. A simple way to ease the implementation and fitting of these models is to incorporate covariate information; some recent work uses directional covariates to explain the variation in a spatial process, for example. We consider a more general framework for modeling how spatial covariances change according to varying geographic features, by way of a covariance regression approach which inherently results in a non-stationary dependence structure. The flexibility of our Bayesian methodology will be described, as well as a computational Markov chain Monte Carlo algorithm for model fitting. We illustrate our approach through analysis of precipitation records.

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