This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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306
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #308010 |
Title:
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A Bayesian Approach to Fitting Mixed Models Using Shape-Restricted Regression Splines
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Author(s):
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Amber Hackstadt*+ and Mary Meyer and Jennifer A. Hoeting
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Companies:
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Colorado State University and Colorado State University and Colorado State University
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Address:
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Colorado State University Dept. of Statistics , Fort Collins, CO, 80523,
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Keywords:
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Bayesian Inference ;
Nonparametric ;
Semiparametric ;
Mixed Models ;
Constrained Regression ;
Isotonic
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Abstract:
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We propose a Bayesian approach to fit mixed models using shape-restricted regression splines. Linear mixed-effects models are often used to analyze repeated measure and longitudinal data but a-priori knowledge about the regression functions is often limited to smoothness and shape (monotone or convex). Shape-restricted splines are used to model regression functions in mixed-effects models and have the advantage of being robust to the number of knots while maintaining good statistical properties. They are flexible but retain a-priori information about the shape and smoothness of the relationships between variables. Simulation studies and examples demonstrate that a Bayesian framework along with vague priors gives function estimates that are similar to maximum likelihood estimates while providing posterior distributions to facilitate inference.
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