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Activity Number:
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37
- Object-Oriented Analysis of Imaging Data
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Type:
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Contributed
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Date/Time:
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Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #302959
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Title:
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Simultaneous Confidence Corridors for Mean Functions in Functional Data Analysis of Imaging Data
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Author(s):
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Yueying Wang* and Guannan Wang and Li Wang and Todd Ogden
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Companies:
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Iowa State University and College of William and Mary and Iowa State University and Columbia University
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Keywords:
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Bivariate splines;
Functional principal component analysis;
Image analysis;
Semiparametric efficiency triangulation
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Abstract:
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Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two-sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies we examine the finite-sample performance of the proposed method. Finally, the proposed method is applied to analyze brain Positron Emission Tomography (PET) data in two different studies. One dataset used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
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Authors who are presenting talks have a * after their name.
