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**

* *

*Large covariance matrix estimation with factor analysis*


*Yuan Liao*


*Department of Mathematics*

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 ***

* *


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]