Abstract Details
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Activity Number:
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474
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #308887 |
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Title:
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Functional Interaction Model
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Author(s):
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Ana-Maria Staicu*+ and Joseph Usset and Arnab Maity
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Companies:
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North Carolina State University and North Carolina State University and North Carolina State University
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Keywords:
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Functional Linear Model ;
Diffusion Tensor Imaging ;
Functional Data Analysis
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Abstract:
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Functional regression with functional predictors is now a well-studied area in functional data analysis. Existing methods, however, are limited to an additive assumption of the covariates effect, and ignore interactions between the functional covariates. We introduce a functional interaction modeling and testing framework where the response depends on the functional covariates and, in addition, on the interaction between them. The methods rely on bases function expansions of both the functional covariates and the coefficient functions, and on the mixed effects representation of the penalized regression. Methodological developments are general, but were inspired by and applied to a Diffusion Tensor Imaging (DTI) brain tractography dataset
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