Abstract Details
Activity Number:
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422
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #309527 |
Title:
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Small Sample Behavior of Generalized Linear Mixed Models with Complex Experiments
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Author(s):
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Julie Couton*+ and Walt W. Stroup
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Companies:
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University of Nebraska and University of Nebraska-Lincoln
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Keywords:
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non-normal data ;
split-plot ;
conditional ;
marginal ;
integral approximation ;
pseudo-likelihood
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Abstract:
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Generalized linear mixed models (GLMMs), regardless of the software used to implement them (R, SAS, etc.), can be formulated as conditional or marginal models and can be computed using pseudo-likelihood, penalized quasi-likelihood, or integral approximation methods. While information exists about the small sample behavior of GLMMs for some cases- notably RCBDs with Binomial or count data- little is known about GLMMs for continuous proportions (e.g. Beta) or time-to-event (e.g. Gamma) data or for more complex designs such as the split-plot. In this presentation we review the major model formulation and estimation options and compare their small sample performance for cases listed above.
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Authors who are presenting talks have a * after their name.
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