|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|
|Date Published||2019 06|
|Keywords||Analysis of Variance, Computer Simulation, Data Interpretation, Statistical, Humans, Longitudinal Studies, Models, Statistical|
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.
|Original Publication||A smoothing-based goodness-of-fit test of covariance for functional data.|
|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.