報(bào)告人：Prof. Chunming Zhang
講座時(shí)間：2015年6月23日上午10:15-11:15
講座地點(diǎn)：犀浦校區(qū)X2511
主講人簡介：
Zhang
Chunming is a professor at Department of Statistics, University ofWisconsin (USA).
She received her Ph.D. in
statistics fromUniversity of North Carolina-Chapel
Hill(USA) in 2000. Her research interests are mainly on Applications
toneuroinformaticsand 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. She 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-).
講座內(nèi)容簡介：
Title:Estimation of the
Error Auto-Correlation Matrix in Semiparametric Models for Brain fMRI Data（在腦功能核磁共振成像數(shù)據(jù)的半?yún)?shù)模型中的誤差自相關(guān)矩陣的估計(jì)）
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.
主辦：研究生院
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