Automatic detection of significant areas for functional data with directional error control. (17th September 2018)

• Record Type: Journal Article
• Authors: Xu, Peirong; Lee, Youngjo; Shi, Jian Qing; Eyre, Janet
• Is Part Of: Statistics in medicine. Volume 38:Number 3(2019)
• Page Start: 376
• Page End: 397
• Publication Date: 2018-09-17
• Subjects: false discovery rate -- functional data -- Gaussian process regression model -- significant areas -- Type III error; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7968
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : 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.