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Dear all,

Below please find the announcement of statistics seminar this week. Please
note that it is not on the usual Friday seminar day.

Seminar Announcement**
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*Large covariance matrix estimation with factor analysis*

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*Yuan Liao*


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*Department of Mathematics*

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University of Maryland, College Park        *

*ENGR. 4201 (CS conference room) *

*4400 University Drive, Fairfax, VA 22030***

*Time: 11:00 A.M. - 12:00 P.M.***

*Date: Thursday, Feb 7, 2013 ***

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*Abstract*

Sparsity is one of the key assumptions to effectively estimate a
high-dimensional covariance matrix. However, in many applications, the
sparsity does not hold due to the existence of some common factors.
Therefore, in practice a more reliable approach for estimating a large
covariance matrix is to first take out the possible common factors before
applying Bickel and Levina (2008)'s thresholding. In this talk, I will give
detailed explanation of the theory and method of high-dimensional factor
analysis. The key feature is that in a high-dimensional factor model, the
covariance matrix has a few very large eigenvalues that diverge fast with
the dimensionality. I will introduce an effective covariance estimator when
sparsity does not hold, called POET, with the help of factor analysis.  Some
immediate applications in finance and econometrics will also be presented.


-- 
Yunpeng Zhao, PhD

Assistant Professor
Department of Statistics
Volgenau School of Engineering <[log in to unmask]>
George Mason University
Engineering Building, Room 1719, MS 4A7
4400 University Drive
Fairfax, VA 22030-4444

Phone: 703-993-1674
Email: [log in to unmask]