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Activity Number: 484
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #309589
Title: Inferential Procedures for Populations of Images
Author(s): Maximillian Chen*+ and Martin T Wells
Companies: Cornell University and Cornell University
Keywords: hypothesis testing ; test statistic ; matrix normal ; likelihood-ratio test ; high-dimensional data
Abstract:

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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