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Activity Number:
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121
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #305845 |
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Title:
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The Generalized Linear Mixed Model with Multinomial Data
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Author(s):
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John Aleong*+
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Companies:
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University of Vermont
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Address:
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, , ,
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Keywords:
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Hierarchical genealized linear model ; spatial statistics ; longitudinal study ; correlation ; model checking
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Abstract:
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The generalized linear mixed models (GLMM) and quasi-likelihood give a flexible framework for analyzing data generated from an exponential family of distributions which includes non-normal data with many types of correlation structures. This theory includes the analysis of discrete and categorical spatial data. Treatment effects in a designed experiment with discrete spatial responses with covariates can be estimated and tested. Universal kriging and indicator kriging are discussed as special cases. Testing model assumptions to discriminate between correlation structures will be demonstrated. Examples are given, on comparing treatments in a designed experiment in which spatial correlation is present.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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