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Activity Number: 37 - Object-Oriented Analysis of Imaging Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #306601
Title: Scalable, Powerful and Robust Basis Space Testing for High-Dimensional Data
Author(s): Ruijin Lu* and Hongxiao Zhu
Companies: Virginia Tech and Virginia Tech
Keywords: Scalable testing; high-dimensional data; functional data testing; testing power; randomized test
Abstract:

With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images and medical spectrum curves. These data are featured with high dimension and high correlations in measurement points, making it desirable to find a fast reliable and powerful approach to extract useful information from the wealth of data. This work focuses on improving the power in the testing of high-dimensional functional data. We consider Westfall-Young Randomization Tests in basis-space via lossless or near-lossless compression. We show that these tests satisfy several nice theoretical properties, including the successful control of family-wise error rate, the improving of power with appropriate truncation, and the asymptotic optimality. The effectiveness of this testing approach is demonstrated using two applications - the detection of regions of the spectrum that are related to pre-cancer using fluorescence spectroscopy data and the detection of disease-related regions using fluorescence spectroscopy data and the detection of disease-related regions using Tensor-Based Morphometry data derived from structural magnetic resonance imaging.


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

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