Title | A smoothing-based goodness-of-fit test of covariance for functional data. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Chen, Stephanie T., Luo Xiao, and Ana-Maria Staicu |
Journal | Biometrics |
Volume | 75 |
Issue | 2 |
Pagination | 562-571 |
Date Published | 2019 Jun |
ISSN | 1541-0420 |
Keywords | Analysis 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. |
DOI | 10.1111/biom.13005 |
Alternate Journal | Biometrics |
Original Publication | A smoothing-based goodness-of-fit test of covariance for functional data. |
PubMed ID | 30450612 |
PubMed Central ID | PMC6526086 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States |
A smoothing-based goodness-of-fit test of covariance for functional data.
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