A0528
Title: Spectral analysis of high-dimensional linear processes with applications
Authors: Debashis Paul - University of California, Davis (United States) [presenting]
Abstract: Results are presented for the limiting behavior of the empirical distribution of eigenvalues of a weighted integral of the sample periodogram for a class of high-dimensional linear processes. The processes under consideration are characterized by having simultaneously diagonalizable coefficient matrices. We make use of these asymptotic results, derived under the setting where the dimension and sample size are comparable, to formulate an estimation strategy for the distribution of eigenvalues of the coefficients of the linear process. This approach generalizes existing works on estimation of the spectrum of an unknown covariance matrix for high-dimensional i.i.d. observations. We discuss various applications of the proposed methodology including in the context of estimation of mean variance frontier in the Markowitz portfolio optimization problem.
