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Activity Number:
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445
- Complex Hypothesis Testing for Spatial, Functional and Neuroimaging Data
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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ENAR
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Abstract #313264
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Title:
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Distribution-Free Pointwise Adjusted P-Values for Functional Hypotheses
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Author(s):
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Philip Reiss* and Meng Xu
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Companies:
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University of Haifa and University of Haifa
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Keywords:
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envelope test; p-value adjustment; functional data analysis; multiple testing; permutation test; magnetic resonance imaging
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Abstract:
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Graphical tests assess whether a function of interest departs from an envelope of functions generated under a simulated null distribution. This approach originated in spatial statistics, but has recently gained some popularity in functional data analysis. Whereas such envelope tests examine deviation from a functional null distribution in an omnibus sense, in some applications we wish to do more: to obtain p-values at each point in the function domain, adjusted to control the family-wise error rate. Here we derive pointwise adjusted p-values based on envelope tests, and relate these to previous approaches for functional data under distributional assumptions. We then present two alternative distribution-free p-value adjustments that offer greater power. The methods are illustrated with an analysis of age-varying sex effects on cortical thickness in the human brain.
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Authors who are presenting talks have a * after their name.
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