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Seminar: Estimation of the Error Auto-Correlation Matrix in Semiparametric
Author:Xiaodong Pan   Jun 16, 2015     Hits:  

Speaker: Prof. Chunming Zhang

Time: 10:15-11:15 am , June 23, 2015

Room: X2511


Zhang Chunming is a professor at Department of Statistics, University of Wisconsin (USA).

He received his Ph.D. in statistics from University of North Carolina-Chapel Hill (USA) in 2000. His research interests are mainly on Applications to neuroinformatics and bioinformatics, Machine learning & data mining, Multiple testing; large-scale simultaneous inference and applications, Statistical methods in financial econometrics, Non- and semi-parametric estimation & inference, Functional & longitudinal data analysis. He severed as an associate editor for Annals of Statistics (2007-2009), and currently serves as an associate editor for Journal of the American Statistical Association (2011-), and an associate editor for Journal of Statistical Planning and Inference (2012-).


   Title: Estimation of the Error Auto-Correlation Matrix in Semiparametric
Models for Brain fMRI Data

Abstract: In statistical analysis of functional magnetic resonance imaging (fMRI), dealing with the temporal correlation is a major challenge in assessing changes within voxels. This paper aims to address this issue by considering a semiparametric model for single-voxel fMRI. 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 rate of convergence and show that the refined estimate converges under an appropriate norm. Numerical results suggest that the refined estimate performs well when it is applied to the detection of the brain activity.                         

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