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