A smoothing-based goodness-of-fit test of covariance for functional data.

TitleA smoothing-based goodness-of-fit test of covariance for functional data.
Publication TypeJournal Article
Year of Publication2019
AuthorsChen, Stephanie T., Luo Xiao, and Ana-Maria Staicu
JournalBiometrics
Volume75
Issue2
Pagination562-571
Date Published2019 06
ISSN1541-0420
KeywordsAnalysis of Variance, Computer Simulation, Data Interpretation, Statistical, Humans, Longitudinal Studies, Models, Statistical
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. We propose a goodness-of-fit test for comparing parametric covariance functions against general nonparametric alternatives for both irregularly observed longitudinal data and densely observed functional data. We consider a smoothing-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 and 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.

DOI10.1111/biom.13005
Alternate JournalBiometrics
Original PublicationA smoothing-based goodness-of-fit test of covariance for functional data.
PubMed ID30450612
PubMed Central IDPMC6526086
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
Project: