 |
 |
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 <[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]