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Activity Number: 282 - Complex Functional Data Analysis with Biomedical Applications
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Nonparametric Statistics
Abstract #309499
Title: Domain Selection for Functional Linear Models: A Dynamic RKHS Approach
Author(s): Jane-Ling Wang*
Companies: UC Davis

In conventional scalar-on-function linear regression model, the entire trajectory of the predictor process on the whole domain is use to model the response variable. However, the response may only be associated with the covariate process X on a subdomain. We consider the problem of estimating the domain of association when assuming that the regression coefficient function is nonzero on a subinterval. We propose a solution based on the reproducing kernel Hilbert space (RKHS) approach to estimate both the domain and the regression function. A simulation study illustrates the effectiveness of the proposed approach. Asymptotic theory is developed for both estimators.

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

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