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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.