Abstract Details
Activity Number:
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368
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #310352 |
Title:
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Manifold Regression for Functional Data
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Author(s):
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Andrew Farris*+ and Hans-Georg G. Müller
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Companies:
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UC Davis and University of California, Davis
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Keywords:
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functional data analysis ;
functional manifold components ;
functional regression ;
dimension reduction
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Abstract:
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The widely used linear methods for regression with functional data are more flexible than analogous linear models for data in finite dimensions. Nonetheless, they require strong assumptions on the linearity of the relationship between predictors and responses. Recent advances have improved upon the linear approach in some cases by taking into account particular data structures, for example, time variation. We propose a more broadly applicable methodology for functional regression, assuming that data lie near an unknown but finite-dimensional manifold.
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Authors who are presenting talks have a * after their name.
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