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Abstract Details
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Activity Number:
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219
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #303659 |
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Title:
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Mixed Additive Models for Function-on-Function Regression
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Author(s):
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Fabian Scheipl*+ and Sonja Greven and Ana-Maria Staicu
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Companies:
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Ludwig-Maxilians-Universität München and Ludwig-Maxilians-Universität München and North Carolina State University
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Address:
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Ludwigstrasse 33, München, International, 80539 , Germany
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Keywords:
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functional data ;
generalized additive models ;
penalized splines ;
mixed models
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Abstract:
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We introduce a general framework for regression of correlated functional responses on scalar and functional covariates. By representing the associated inference problem in terms of an additive mixed model for scalar response, we are able to make use of established estimation algorithms to fit complex and versatile models for functional responses. We discuss approaches to model spatial, temporal, spatio-temporal and hierachical correlation structures of the functional responses in this framework and present results from simulation studies and application examples.
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