This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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129
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #308091 |
Title:
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Predictions in Generalized Linear Mixed Model
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Author(s):
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Chenghsueh Yang*+
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Companies:
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University of California, Riverside
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Address:
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1456 everton pl, riverside, CA, 92507,
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Keywords:
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Generalized linear mixed model ;
Pseudo-Likelihood ;
Newton-Raphson ;
Laplace approximation ;
Empirical best linear predictor ;
Gauss-Hermite quadrature
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Abstract:
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A randomized clinical trial design is used as an example for comparing effects of drugs. Constructing Generalized linear mixed model (GLMM) can tell different effects between drugs. However, GLMM confronts a problem of maximizing the high-dimensional integration of the likelihood function. Wolfinger (1993) proposed both Pseudo-Likelihood and Restricted Pseudo-Likelihood to tackle this problem. Both methods are based on a pseudo variable followed by application of the Newton-Raphson algorithm. McCulloch (2001) suggested applying the Gauss-Hermite quadrature for approximation of the likelihood function. Laplace approximations have also been proposed for approximating the integrals in closed form. More recently, a modified Laplace approximation has been proposed. In this talk I will also discuss the mean squared error performance of the empirical best linear predictor.
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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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