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Activity Number: 485
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318853 View Presentation
Title: Single-Index Models for Function-on-Function Regression
Author(s): Guanqun Cao* and Li Wang
Companies: Auburn University and Iowa State University
Keywords: B-spline ; Functional data ; Functional regression model ; Multiple functional predictors ; Single-index model

We develop a functional single-index model for functional data analysis, where both the response and multiple predictors are functions, and we assume the response is related to a linear combination of the predictors via an unknown link function. The proposed method greatly enhances the flexibility of functional linear models and provides a useful tool for dimension reduction in regression with multiple functional predictors. Assuming that the functional predictors are observed at discrete points, we use B-spline basis functions to estimate the index functions and the link function, and propose an iterative estimating procedure. Several numerical examples illustrate that the proposed model and estimation methodology are flexible and effective in practice.

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

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