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Activity Number: 132 - Functional Data and Time Series
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #301797
Title: Estimation and Inference for Functional Linear Regression Models with Varying Regression Coefficients
Author(s): Guanqun Cao* and Li Wang and Shuoyang Wang
Companies: Auburn University and Iowa State University and Auburn University
Keywords: B-spline; Functional data ; Function-on-Function regrerssion; Multiple functional predictors

In this work, motivated by a real data example, we present a class of functional linear regression models of a functional response on one or multiple functional predictors and scalar predictors. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple scalar and functional predictors. The functional response is observed on a dense or sparse grid. Tensor product B-spline basis is proposed for the estimation of the bivariate coefficient functions. It also allows for the integration of continuous or categorical covariates. We show that our estimators hold asymptotic consistency and normality. Several numerical examples demonstrate a superior performance of the proposed methods against several existing approaches. The proposed method is also applied to the motivating example.

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

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