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Abstract Details
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Activity Number:
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518
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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| Abstract - #303118 |
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Title:
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Empirical-Bayesian Inference for Count Data Using the Spatial Random Effects Model
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Author(s):
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Aritra Sengupta*+ and Noel Cressie
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Companies:
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The Ohio State University and The Ohio State University
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Address:
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1958 Neil Avenue, Columbus, OH, 43210, United States
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Keywords:
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Poisson model ;
geostatistical process ;
maximum-likelihood ;
EM algorithm ;
method-of-moments ;
MCMC
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Abstract:
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This paper is concerned with inference for spatial data in the form of counts. We consider a Poisson model for the counts, and assume an underlying geostatistical process for the mean of the Poisson distribution. We develop maximum-likelihood estimates for the parameters of the continuous process using the expectation-maximization (EM) type algorithm. The starting value for the EM algorithm is critical, and we obtain our starting values using method-of-moment estimators. The expectations in the E-step of the EM algorithm are not available in closed form, so we use some numerical and theoretical approximations to the expectations required in the E-step. Empirical-Bayesian inference, based on an MCMC, will then be discussed.
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