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

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.

SUBMITTER: Chen ST

PROVIDER: S-EPMC6526086 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Chen Stephanie T ST Xiao Luo L Staicu Ana-Maria AM

Biometrics 20190406 2

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 ...[more]

PMID: 30450612

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Dataset Information

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

Publications

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

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