Fixed-effects inference and tests of correlation for longitudinal functional data

Ruonan Li; Luo Xiao; Ekaterina Smirnova; Erjia Cui; Andrew Leroux; Ciprian M. Crainiceanu

doi:10.1002/sim.9421

Fixed-effects inference and tests of correlation for longitudinal functional data

Ruonan Li, Luo Xiao, Ekaterina Smirnova, Erjia Cui, Andrew Leroux, Ciprian M. Crainiceanu

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal sampling procedure. The framework provides a natural extension of standard longitudinal correlation models for scalar observations to functional observations. Using simulation studies, we compare fixed effects estimation under correctly and incorrectly specified correlation structures and also test the longitudinal correlation structure. Finally, we apply the proposed methods to a longitudinal functional dataset on physical activity. The computer code for the proposed method is available at https://github.com/rli20ST758/FILF.

Original language	English (US)
Pages (from-to)	3349-3364
Number of pages	16
Journal	Statistics in Medicine
Volume	41
Issue number	17
DOIs	https://doi.org/10.1002/sim.9421
State	Published - Jul 30 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Keywords

accelerometry data
covariance function
hypothesis test
mixed effects model

PubMed: MeSH publication types

Journal Article
Research Support, N.I.H., Extramural

Publisher link

10.1002/sim.9421

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Cite this

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AU - Xiao, Luo

AU - Smirnova, Ekaterina

AU - Cui, Erjia

AU - Leroux, Andrew

AU - Crainiceanu, Ciprian M.

PY - 2022/7/30

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KW - accelerometry data

KW - covariance function

KW - hypothesis test

KW - mixed effects model

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