Online Program Home
My Program

Abstract Details

Activity Number: 225 - The Interface of Functional Data Analysis and Biomedical Applications
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #329698 Presentation
Title: A Bootstrap-Based Goodness-of-Fit Test of Covariance for Functional Data
Author(s): Luo Xiao* and Stephanie Chen and Ana-Maria Staicu
Companies: North Carolina State University and North Carolina State University and NC State University
Keywords: Longitudinal data; Functional data; Smoothing; Testing

Functional data methods are often applied to longitudinal data as they provide a more flexible way to capture dependence across repeated observations. However, there is no formal testing procedure to determine if functional methods are actually necessary. In this paper, we propose a goodness-of-fit test for comparing parametric covariance functions against general nonparametric alternatives for sparsely observed longitudinal data and densely observed functional data. We consider a distance-based test statistic and approximate its null distribution using a bootstrap procedure. We focus on testing a quadratic polynomial covariance induced by a linear mixed effects model, but the method can be used to test any smooth parametric covariance function. Performance and versatility of the proposed test is illustrated through a simulation study and three data applications.

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

Back to the full JSM 2018 program