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Automatic detection of significant areas for functional data with directional error control

Lookup NU author(s): Dr Jian Shi, Professor Janet Eyre


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© 2018 John Wiley & Sons, Ltd. In this paper, we propose a large-scale multiple testing procedure to find the significant sub-areas between two samples of curves automatically. The procedure is optimal in that it controls the directional false discovery rate at any specified level on a continuum asymptotically. By introducing a nonparametric Gaussian process regression model for the two-sided multiple test, the procedure is computationally inexpensive. It can cope with problems with multidimensional covariates and accommodate different sampling designs across the samples. We further propose the significant curve/surface, giving an insight on dynamic significant differences between two curves. Simulation studies demonstrate that the proposed procedure enjoys superior performance with strong power and good directional error control. The procedure is also illustrated with the application to two executive function studies in hemiplegia.

Publication metadata

Author(s): Xu P, Lee Y, Shi JQ, Eyre J

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2019

Volume: 38

Issue: 3

Pages: 376-397

Print publication date: 10/02/2019

Online publication date: 17/09/2018

Acceptance date: 22/08/2018

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/sim.7968


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