research-article

Permuting longitudinal data in spite of the dependencies

Authors:

Sarah Friedrich,

Markus PaulyAuthors Info & Claims

Journal of Multivariate Analysis, Volume 153, Issue C

Pages 255 - 265

https://doi.org/10.1016/j.jmva.2016.10.004

Published: 01 January 2017 Publication History

Abstract

For general repeated measures designs the Wald-type statistic (WTS) is an asymptotically valid procedure allowing for unequal covariance matrices and possibly non-normal multivariate observations. The drawback of this procedure is its poor performance for small to moderate samples, i.e., decisions based on the WTS may become quite liberal. It is the aim of the present paper to improve the small-sample behavior of the WTS by means of a novel permutation procedure. In particular, it is shown that a permutation version of the WTS inherits its good large-sample properties while yielding a very accurate finite-sample control of the type-I error as shown in extensive simulations. Moreover, the new permutation method is motivated by a practical data set of a split plot design with a factorial structure on the repeated measures.

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Cited By

Fielder MWolf EDöllinger NMal DBotsch MLatoschik MWienrich C(2024)From Avatars to Agents: Self-Related Cues Through Embodiment and Personalization Affect Body Perception in Virtual RealityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345621130:11(7386-7396)Online publication date: 1-Nov-2024
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Rühl JFriedrich S(2024)Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risksStatistics and Computing10.1007/s11222-024-10420-w34:3Online publication date: 21-Mar-2024
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Published In

cover image Journal of Multivariate Analysis

Journal of Multivariate Analysis Volume 153, Issue C

January 2017

205 pages

ISSN:0047-259X

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Copyright © Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 January 2017

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Cited By

Fielder MWolf EDöllinger NMal DBotsch MLatoschik MWienrich C(2024)From Avatars to Agents: Self-Related Cues Through Embodiment and Personalization Affect Body Perception in Virtual RealityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345621130:11(7386-7396)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456211
Rühl JFriedrich S(2024)Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risksStatistics and Computing10.1007/s11222-024-10420-w34:3Online publication date: 21-Mar-2024
https://dl.acm.org/doi/10.1007/s11222-024-10420-w
Basak SPauly MKumar S(2024)Adaptive tests for ANOVA in Fisher–von Mises–Langevin populations under heteroscedasticityComputational Statistics10.1007/s00180-022-01298-139:2(433-459)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00180-022-01298-1
Döllinger NWolf EBotsch MLatoschik MWienrich C(2023)Are Embodied Avatars Harmful to our Self-Experience? The Impact of Virtual Embodiment on Body AwarenessProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580918(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580918
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