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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 - #303905 |
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Title:
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Function-on-Scalar Regression with the Refund Package
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Author(s):
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Philip Reiss*+ and Lei Huang
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Companies:
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New York University and The Johns Hopkins University
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Address:
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215 Lexington Ave., 16th floor, New York, NY, 10016,
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Keywords:
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coefficient function ;
functional responses ;
multidimensional smoothing ;
smoothing parameters ;
varying-coefficient model
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Abstract:
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The R package "refund" implements a number of recently developed methods for REgression with FUNctional Data, where the responses, the predictors, or both may be functional. This talk will focus on methods for regressing functional responses on scalar predictors. Key issues include accounting for within-function dependence in the responses; optimal smoothing for multiple coefficient functions; fixed-effect vs. mixed-effect modeling; and frequentist vs. Bayesian interval estimation. We shall also discuss models that allow for nonlinear dependence on the predictors. A neuroimaging application illustrates how the methodology can yield novel inferences about brain development.
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