Online Program Home
My Program

Abstract Details

Activity Number: 125
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #320405
Title: Tensor Regression with Functionally Linked Parameters
Author(s): Su Chen* and Ebenezer Olusegun George
Companies: University of Memphis and University of Memphis
Keywords: Tensor Regression ; Multi-dimensional arrays ; High dimension ; Medical imaging

The rapid development of medical imaging technologies, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), allows researchers to predict disease status/clinical outcomes by analyzing these images. Classical regression methods turn the images stored as multi-dimensional arrays into a vector. This process not only damages the structure of the image data but also has been challenged by ultra-high dimensionality. Traditional tensor regression substantially reduces the number of parameters to be estimated and facilitates the capturing of the structure of data via the product-linked parameters. We propose a tensor regression model with functionally linked parameters, which can characterize more complicated structural correlations in images.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association