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Abstract Details
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Activity Number:
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21
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #300745 |
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Title:
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Directional Weights Car Models Using Gaussian Process Mixing
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Author(s):
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Veronica J. Berrocal*+ and Alan E. Gelfand
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Companies:
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University of Michigan and Duke University
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Address:
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Department of Biostatistics, Ann Arbor, MI, 48109, US
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Keywords:
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Conditionally autoregressive model ;
Gaussian process ;
non-stationarity
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Abstract:
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Areal or lattice data is commonly analyzed using conditionally autoregressive (CAR) models. In a CAR specification the weighting scheme used to derive the full conditionals is constant over space and fixed a priori to be either binary and determined by the adjacency relationship among subregions/cells, or inversely proportional to the distances among subregions/cells. In this paper, we propose an extension of the CAR modeling framework where the weights are non-constant and are random variables obtained by mixing a latent Gaussian process. The resulting class of directional-weight CAR models is flexible, allows for non-stationarity and generalizes the class of fixed-weight CAR models, which arise as a special case of our directional-weights CAR models.
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