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Activity Number:
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361
- Contributed Poster Presentations: Section on Nonparametric Statistics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #314423
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Title:
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Nonlinear Function on Function Regression using RKHS framework
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Author(s):
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Bahaeddine Taoufik*
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Companies:
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Saint Joseph's University
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Keywords:
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Functional Data;
Nonlinear functional regression;
RKHS
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Abstract:
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This work deals with nonlinear regression models in functional data involving an additive functional regression model. In this model, the relationship between a functional response and a functional covariate is determined by an unknown trivariate nonlinear regression function assumed to be in a Reproducing Kernel Hilbert Spaces (RKHS). The estimation procedure as well simulation studies are presented to investigate the prediction performance of the model. Furthermore, how this work can be extended to a nonlinear additive regression model with multiple covariates is discussed. This new model leads to the estimation of an unknown multivariate nonlinear regression function using RKHS framework. Use of this model carries the potential to be applied to financial data where an individual stock is the functional response and multiple U.S market indexes are the functional covariates considered in the new nonlinear functional regression.
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Authors who are presenting talks have a * after their name.
