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Activity Number: 133 - Statistical Methods for Functional Data
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #304700 Presentation
Title: Covariance Based Low-Dimensional Registration for Function-On-Function Regression
Author(s): Tobia Boschi* and Francesca Chiaromonte and Piercesare Secchi and Bing Li
Companies: 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
Keywords: Function-on-Function regression; Functional Data Registration; Covariance Operator

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.

Authors who are presenting talks have a * after their name.

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