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20162022
most citedAlmost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices

24 citations · 34 across the 6 of their papers we have counts for

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8 papers · 1 filter

math.PR20221 cited

Large Sample Covariance Matrices of Gaussian Observations with Uniform Correlation Decay

Michael Fleermann, Johannes Heiny

We derive the Marchenko-Pastur (MP) law for sample covariance matrices of the form , where is a data matrix and as $n,p…

math.PR2022

Large sample correlation matrices: a comparison theorem and its applications

Johannes Heiny

In this paper, we show that the diagonal of a high-dimensional sample covariance matrix stemming from independent observations of a -dimensional time series with finite four…

math.PR2021

Thin-shell theory for rotationally invariant random simplices

Johannes Heiny, Samuel Johnston, Joscha Prochno

For fixed functions , consider the rotationally invariant probability density on of the form \[ μ^n(ds) = \frac{1}{Z_n} G(\|s\|_2)\, e^{…

math.PR2020

Point process convergence for the off-diagonal entries of sample covariance matrices

Johannes Heiny, Thomas Mikosch, Jorge Yslas

We study point process convergence for sequences of iid random walks. The objective is to derive asymptotic theory for the extremes of these random walks. We show convergence of th…

math.PR202024 cited

Almost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices

Johannes Heiny, Thomas Mikosch

In this paper, we show that the largest and smallest eigenvalues of a sample correlation matrix stemming from independent observations of a -dimensional time series with iid…

math.PR20209 cited

The eigenstructure of the sample covariance matrices of high-dimensional stochastic volatility models with heavy tails

Johannes Heiny, Thomas Mikosch

We consider a -dimensional time series where the dimension increases with the sample size . The resulting data matrix follows a stochastic volatility model: each entr…