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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 #305188 Presentation
Title: Nonlinear Function-On-Function Regression Model Using Reproducing Kernel Hilbert Spaces Method
Author(s): Bahaeddine Taoufik* and Matthew Reimherr and Bharath Sriperumbudur
Companies: Saint Joseph's University and Penn State University and The Pennsylvania State University
Keywords: functional data analysis; functional regression; reproducing kernel hilbert spaces; nonlinear regression

In this work, we present an additive nonlinear function-on-function regression model, a type of nonlinear model that uses an additive relationship between the functional response and the functional covariate. We utilize the Reproducing Kernel Hilbert Spaces approach to estimate a regression function that is nonlinear in the functional covariate. We establish optimal rates of convergence for our estimates in terms of prediction error. This work arises computational challenges due to the complexity of the nonlinear functional regression model. Simulations and application to Cumulative Intraday Returns around the 2008 financial crisis are also provided and discussed.

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

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