Back to Search
Start Over
Multivariate functional responses low rank regression with an application to brain imaging data
- Publication Year :
- 2020
-
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 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.<br />Comment: Canadian Journal of Statistics(accepted)
- Subjects :
- Statistics - Methodology
Statistics - Applications
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2010.03700
- Document Type :
- Working Paper