Abstract Details
Activity Number:
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344
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 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 #312201
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View Presentation
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Title:
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Spatial Prediction of Poisson Response Variable with Covariate
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Author(s):
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Lynette M. Smith*+ and David B. Marx
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Companies:
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University of Nebraska Medical Center and University of Nebraska
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Keywords:
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spatial prediction ;
Poisson ;
NORTA
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Abstract:
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It is often of interest to predict spatially correlated count data such as that arising from disease incidence or mortality rates. A generalized linear mixed model (GLMM) approach to prediction using Poisson response variable conditional on the spatial location is simulated using G-side models. We simulated data from a Poisson distribution with a spherical correlation structure and separately co-simulated covariates correlated with the original variable from Gaussian, Binomial and Beta distributions. This was accomplished using NORTA (Normal to Anything) after simulating a bivariate spatial Gaussian structure. We compared prediction of unobserved spatial locations under various conditions: entire response variable (Poisson) available or fractions of it missing and the entire covariate (Gaussian, Binomial or Beta) or some of it missing. We also fit a multivariate GLMM with both the Poisson variable and the covariate as outcome variables to compare its prediction with the other scenarios as described. The addition of a covariate improved prediction in the GLMM models, as expected. However, the comparison of interest is looking at the effect of the various covariate distributions.
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Authors who are presenting talks have a * after their name.
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