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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #310352
Title: Manifold Regression for Functional Data
Author(s): Andrew Farris*+ and Hans-Georg G. Müller
Companies: UC Davis and University of California, Davis
Keywords: functional data analysis ; functional manifold components ; functional regression ; dimension reduction
Abstract:

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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