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 __

** **

**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

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]