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Activity Number: 56 - Modern Methods for Structured and Dynamically Dependent Data
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #308081
Title: Spectral analysis of a class of high-dimensional linear processes
Author(s): Debashis Paul*
Companies: University of California, Davis
Keywords:
Abstract:

We present results about the limiting behavior of the empirical distribution of eigenvalues of weighted sums 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 an application of the proposed methodology in the context of estimation of mean-variance frontier in the Markowitz portfolio optimization problem. (This is a joint work with Jamshid Namdari, Haoyang Liu and Alexander Aue.)


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