This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #308914
Title: The Generalized Linear Mixed Model with Spatial-Temporal Data
Author(s): J Aleong*+
Companies: University of Vermont
Address: , Burlington, 05405,
Keywords: Hierarchical genealized linear model ; spatial statistics ; longitudinal study ; split plots, ; model checking

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. Testing model assumptions to discriminate between correlation structures will be demonstrated. Examples are given, on comparing treatments in a designed experiment with spatial-temporal data.

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