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Abstract Details
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Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305077 |
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Title:
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Regularized Multidimensional Functional Regression via Haar Wavelets with Application to PET Imaging
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Author(s):
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Xuejing Wang*+ and Bin Nan and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Address:
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2023 Medford Rd., Ann Arbor, MI, 48104, United States
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Keywords:
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Haar wavelet ;
functional regression ;
variable selection
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Abstract:
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In recent year, samples of curves or images are more often to be collected in many fields. Our primary motivation and application come from brain imaging studies in subjects with Alzheimer's disease (AD)in comparison to healthy subjects. We propose an efficient Haar wavelet-based approach for the analysis of 3D brain image data in the framework of functional data analysis, which automatically take into account the spatial structures. The main aim is to find the voxels that are most relevant to the severity of AD in terms of prediction accuracy. The performance of the proposed approach is evaluated through extensive simulation studies and real PET image data. The findings are consistent with most previous studies, and selected features correspond to meaningful regions of the human brain.
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