Southwest Jiaotong University School of Mathematics


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创源学术讲座:Estimation of the Error Auto-Correlation Matrix in Semiparametric

ag网站亚游登录:   作者:潘小东     日期:2015-06-16 00:00:00   点击数:  

报告人:Prof. Chunming Zhang




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-).


Title:Estimation of the Error Auto-Correlation Matrix in Semiparametric Models for Brain fMRI Data(在脑功能核磁共振成像数据的半参数模型中的误差自相关矩阵的估计)

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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