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Activity Number:
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133
- Statistical Methods for Functional Data
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 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 #304700
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Presentation
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Title:
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Covariance Based Low-Dimensional Registration for Function-On-Function Regression
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Author(s):
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Tobia Boschi* and Francesca Chiaromonte and Piercesare Secchi and Bing Li
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Companies:
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Pennsylvania State University and Pennsylvania State University and EMbeDS, Sant'Anna School of Advanced Studies and Politecnico di Milano, MOX Laboratory for Modeling and Scientific Computing and The Pennsylvania State University
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Keywords:
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Function-on-Function regression;
Functional Data Registration;
Covariance Operator
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Abstract:
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We propose a new low-dimensional registration procedure that exploits the relationship between response and predictor in a function-on-function regression. In this context, Functional Covariance Components (FCC) provide a flexible and powerful tool to represent the data in a low-dimensional space, capturing the most meaningful modes of dependency between the two set of curves. Based on this reduced representation, our procedure aligns simultaneously the two sets of curves, in a way that optimizes the subsequent regression analysis. To implement our procedure, we use both the Continuous Registration algorithm (CR) and a novel parallel algorithm coded in R. We then compare it to other common registration approaches via simulations and an application to the AneuRisk data.
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Authors who are presenting talks have a * after their name.
