Estimation of the Error Correlation Matrix in Semiparametric Models for Brain fMRI Data

• Activity Number: 431
• Type: Invited
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Chunming Zhang*+ and Xiao Guo
• Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
• Keywords: fMRI ; inverse ; semiparametric model

Abstract

In statistical analysis of functional magnetic resonance imaging (fMRI), dealing with the temporal correlation is a major challenge in assessing changes within voxels. In this paper, we aim to address this issue by considering a semi-parametric model for fMRI data. For the error process in the semi-parametric model, we construct a banded estimate of the auto-correlation matrix R, and propose a refined estimate of the inverse of R. Under some mild regularity conditions, we establish consistency of the banded estimate with an explicit convergence rate and show that the refined estimate converges under an appropriate norm. Numerical results suggest that the refined estimate performs conceivably well when it is applied to the detection of the brain activity.