Abstract Details
Activity Number:
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226
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #312520
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Title:
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Spatial-Temporal Functional Principal Component Analysis and Its Application on fMRI
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Author(s):
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Lei Huang*+ and Philip T. Reiss and Luo Xiao and Martin Lindquist and Ciprian Crainiceanu
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Companies:
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Johns Hopkins University and New York University School of Medicine and Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University
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Keywords:
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FPCA ;
spatial-temporal structure ;
fMRI
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Abstract:
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Functional principal component analysis (FPCA) is a basic tool for dimension reduction in functional data analysis. Many examples of massive data, however, have additional structure that is ignored by standard approaches to FPCA, such as the spatio-temporal structure of functional MRI (fMRI) data. We propose generalized models of FPCA that are appropriate for massive data sets with known two-way dependencies. We discuss identifiability conditions and develop estimation procedures for each model. The methodology is motivated by, and is applied to, an fMRI study designed to analyze the relationship between pain and brain activity.
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Authors who are presenting talks have a * after their name.
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