|
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
|
|
Abstract:
|
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.
