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 

 

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]