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Activity Number: 532 - Making Big and Complex Imaging Data Count with New Statistical Tools
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #309199
Title: Multivariate Functional Responses Low Rank Regression with an Application to Brain Imaging Data
Author(s): Xiucai Ding and Dengdeng Yu and Zhengwu Zhang and Dehan Kong*
Companies: Duke University and University of Toronto and University of Rochester and University of Toronto
Keywords: functional data; functional magnetic resonance imaging; low rank; Sieve regression
Abstract:

We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions on a set of sieve basis, we reconstruct the basis coefficients as a matrix. To estimate these coefficients, we develop an efficient estimation procedure using nuclear norm regularization. We derive error bounds for our estimates and evaluate our method using simulations. We further apply our method to the Human Connectome Project neuroimaging data, where we predict cortical surface emotion task-evoked functional magnetic resonance imaging signals using various clinical covariates.


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

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