This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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499
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #306746 |
Title:
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Functional Mixed Effects Modeling by Parameter Cascading
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Author(s):
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Jiguo Cao*+ and James Owen Ramsay
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Companies:
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Simon Fraser University and McGill University
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Address:
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Department of Statistics and Actuarial Science, Burnaby, BC, V5A1S6 , Canada
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Keywords:
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marginalization ;
regularization ;
nuisance parameters ;
mixed effect smoothing
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Abstract:
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A linear mixed effects model (LME) is a familiar example of a multi-level parameter structure involving nuisance and structural parameters, as well as parameters that essentially control the model's complexity. Marginalization over nuisance parameters, such as the restricted maximization likelihood method, has been the usual estimation strategy; but can involve onerous and complex algorithms to achieve the integrations involved. Parameter cascading is described as a multi-criterion optimization algorithm that is relatively simple to program and leads to fast and stable computation. The method is applied to functional mixed effects modeling by introducing a basis partitioning scheme and defining roughness penalty terms for both functional fixed effect and random effects. The results are substantially better than those obtained by using the previous LME methods.
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