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Abstract Details
Activity Number:
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354
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract - #302434 |
Title:
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A Simple and Efficient Reparameterization for Mixed-Effects Models
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Author(s):
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Guangxiang Zhang*+ and John J. Chen
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Companies:
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The State University of New York at Stony Brook and Stony Brook University
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Address:
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, , NY, 11764, USA
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Keywords:
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Mixed-effects or multilevel models ;
Convergence rate ;
Collinearity between random-effects ;
Centering ;
Optimal linear transformation ;
Random slope
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Abstract:
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Linear mixed-effects model has been widely used in hierarchical and longitudinal data analyses. In practice, the fitting algorithm can fail to converge due to boundary issues of the estimated random-effects covariance matrix G. Current available algorithms are not computationally optimal because the condition number of G is unnecessarily increased when the random-effects correlation estimate is not zero. The traditional mean centering technique may even increase the random-effects correlation. To improve the convergence of data with such boundary issue, we propose an adaptive fitting (AF) algorithm using an optimal linear transformation of the random-effects design matrix. The AF algorithm can be easily implemented with standard software and be applied to other mixed-effects models. Simulations show that AF significantly improves the convergence rate, and reduces the condition number and non-positive definite rate of the estimated G, especially under small sample size, relative large noise and high correlation settings. One real life data for Insulin-like Growth Factor (IGF) protein is used to illustrate the application of this algorithm implemented with software package R (nlme).
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