Estimation of the Error Autocorrelation Matrix in Semiparametric Models for fMRI Data

Abstract

[evdate 2013-12-19] HKUST International Forum on Probability and Statistics. Talk no.15. Title from opening screen.

The Second HKUST International Forum on Probability and Statistics (2013), held 19 December, 2013, at the Hong Kong University of Science and Technology. Co-sponsored by the HKUST Jockey Club Institute for Advanced Study and the Center for Statistical Science.

'Based on joint work with Xiao Guo.'

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 the talk, the speaker aims to address this issue by considering a semi-parametric model for fMRI data. For the error process in the semi-parametric model, and 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, the speaker 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.

Duration: 40 min.