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Activity Number: 161 - SPEED: Nonparametrics and Imaging
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #324958 View Presentation
Title: A Comparison of Testing Methods in Scalar-On-Function Regression
Author(s): Merve Tekbudak* and Marcela Alfaro Córdoba and Ana-Maria Staicu and Arnab Maity
Companies: North Carolina State University and North Carolina State University and North Carolina State University, Department of Statistics and North Carolina State University
Keywords: Functional linear model ; Functional regression ; Nonparametric model ; Mixed models ; Hypothesis Testing

A scalar-response functional model describes the association between a scalar response and a set of functional covariates. An important problem in the functional data literature is to test the nullity or linearity of the effect of the functional covariate in the context of scalar-on-function regression. This article provides an overview of existing methods for testing both the null hypotheses that there is no relationship and linear relationship between the functional covariate and scalar response. Our primary interest is to compare numerically the performance of the methods in an extensive simulation study. We assess the performance of the methods under the assumption that the functional covariate may be observed on a dense, moderately sparse or sparse grid, under several scenarios with and without measurement error, and different sample sizes. We further illustrate the methods on the Tecator data set.

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

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