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Abstract Details
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Activity Number:
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622
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306360 |
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Title:
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Prediction Intervals Based on the Best Linear Predictor for Generalized Linear Mixed Models
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Author(s):
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Chenghsueh Yang*+ and Daniel Jeske
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Companies:
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University of California at Riverside and University of California at Riverside
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Address:
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1456 Everton Pl, Riverside, CA, 92507, United States
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Keywords:
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generalized linear mixed model ;
Pseudo-likelihood ;
Baysian ;
best linear prediction ;
empirical
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Abstract:
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We propose a new method for deriving prediction intervals for generalized linear mixed models, based on best linear prediction. Two existing methods in the literature are based on Bayesian and pseudo-likelihood approaches. We compare the different approaches in the context of several illustrative generalized linear mixed models using coverage probability as performance criteria.
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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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