Abstract Details
Activity Number:
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484
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #309589 |
Title:
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Inferential Procedures for Populations of Images
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Author(s):
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Maximillian Chen*+ and Martin T Wells
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Companies:
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Cornell University and Cornell University
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Keywords:
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hypothesis testing ;
test statistic ;
matrix normal ;
likelihood-ratio test ;
high-dimensional data
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Abstract:
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The population value decomposition (PVD) by Crainiceanu et al (2011) is a method for dimension reduction of a population of massive images. Images are decomposed into a product of two orthogonal matrices with population-specific features and one matrix with subject-specific features. The problem of inference in the PVD framework has yet to be solved. In order to develop our inferential procedures, we assume our data to be matrix normally distributed. We will introduce likelihood-ratio tests for the one- and two-population problems and derive the distributions of the resulting test statistics. Practical implementation of the method will be illustrated.
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Authors who are presenting talks have a * after their name.
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