Abstract Details
Activity Number:
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379
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #311962
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Title:
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Penalized Quasi-Likelihood Estimating Equations and Variable Selection for Spatial Data
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Author(s):
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Chae Young Lim*+ and Wenning Feng and Tapabrata Maiti
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Companies:
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Michigan State University and Michigan State University and Michigan State University
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Keywords:
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Estimating Equations ;
Variable selection ;
Spatial data
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Abstract:
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Variable selection in a spatial regression model is challenging because of the spatial correlation structure in the error term of the regression model that needs to be accounted for. A number of studies have been published on the variable selection in a linear spatial regression model. Compared to the spatial linear models, the likelihood-based estimation, and thus the variable selection, is not trivial for the spatial generalized linear models. In this work, we consider a variable selection method via the penalized quasi-likelihood estimating equation for the spatial generalized linear models. The methods are applied to health monitoring data for the selection of factors that affect the outcomes which are of the forms of binary or counts.
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Authors who are presenting talks have a * after their name.
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