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Multivariate functional response low‐rank regression with an application to brain imaging data
Author(s) -
Ding Xiucai,
Yu Dengdeng,
Zhang Zhengwu,
Kong Dehan
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11604
Subject(s) - multivariate statistics , covariate , human connectome project , functional magnetic resonance imaging , regression , neuroimaging , computer science , regularization (linguistics) , functional data analysis , multivariate analysis , artificial intelligence , pattern recognition (psychology) , mathematics , statistics , machine learning , psychology , neuroscience , functional connectivity
We propose a multivariate functional response low‐rank regression model with possible high‐dimensional functional responses and scalar covariates. By expanding the slope functions on a set of sieve bases, we reconstruct the basis coefficients as a matrix. To estimate these coefficients, we propose an efficient procedure using nuclear norm regularization. We also derive error bounds for our estimates and evaluate our method using simulations. We further apply our method to the Human Connectome Project neuroimaging data to predict cortical surface motor task‐evoked functional magnetic resonance imaging signals using various clinical covariates to illustrate the usefulness of our results.