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Activity Number: 449
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 2:45 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321684
Title: Modern Projection Pursuit Ellipse for High-Dimensional Data
Author(s): Jang Ik Cho* and Xiaoyan Wei and Jiayang Sun
Companies: Case Western Reserve University and Case Western Reserve University and Case Western Reserve University
Keywords: covariance ; projection pursuit ; high-dimensional data ; large p data

Many standard multivariate techniques were developed based on the assumption that the covariance matrices from different groups are equal. A well-known test for testing the equality of covariance is the Bartlett's test. However the Bartlett's test is only a function of the volumes of covariance matrices not accounting for their shapes and orientations and only for p< < n cases. In this work we developed a Projection Pursuit Ellipses (PPE) for high-dimensional data and compared its performance with the Bartlett's test and a modern benchmark for high dimensional-p data.

Authors who are presenting talks have a * after their name.

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