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Spectral estimation for a class of high-dimensional linear processes

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STSW02 - Statistics of geometric features and new data types

We present results about the limiting behavior of the empirical distribution of eigenvalues of weighted integrals 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.  

(Joint work with Jamshid Namdari and Alexander Aue)

This talk is part of the Isaac Newton Institute Seminar Series series.

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