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Automatic detection of significant areas for functional data with directional error control.
Xu, Peirong; Lee, Youngjo; Shi, Jian Qing; Eyre, Janet.
Afiliación
  • Xu P; College of Mathematics and Sciences, Shanghai Normal University, Shanghai, China.
  • Lee Y; Department of Statistics, Seoul National University, Seoul, Korea.
  • Shi JQ; School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK.
  • Eyre J; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
Stat Med ; 38(3): 376-397, 2019 02 10.
Article en En | MEDLINE | ID: mdl-30225994
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Interpretación Estadística de Datos / Reacciones Falso Positivas Tipo de estudio: Diagnostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2019 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Interpretación Estadística de Datos / Reacciones Falso Positivas Tipo de estudio: Diagnostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2019 Tipo del documento: Article País de afiliación: China