Inferential Methods for Populations of Images

• Activity Number: 655
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Maximillian Chen*+ and Martin Wells
• Companies: Cornell University and Cornell University
• Keywords: hypothesis testing ; matrix normal ; high-dimensional data ; likelihood-ratio test ; score test ; regression-based inference

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, score tests, and regression-based test for the one-, two-, and k-population problems and derive the distributions of the resulting test statistics. Practical implementation of the method will be illustrated.