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20162020
most citedAlmost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices

24 citations · 33 across the 3 of their papers we have counts for

collaborators

5 papers

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.ST2020

Large sample autocovariance matrices of linear processes with heavy tails

Johannes Heiny, Thomas Mikosch

We provide asymptotic theory for certain functions of the sample autocovariance matrices of a high-dimensional time series with infinite fourth moment. The time series exhibits lin…

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…

math.PR2019

High-dimensional sample covariance matrices with Curie-Weiss entries

Michael Fleermann, Johannes Heiny

We study the limiting spectral distribution of sample covariance matrices , where are random matrices with correlated entries, for the cases $p/n\to y\in [0,\…

math.ST2016

Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series

Richard Davis, Johannes Heiny, Thomas Mikosch +1

We provide some asymptotic theory for the largest eigenvalues of a sample covariance matrix of a p-dimensional time series where the dimension p = p_n converges to infinity when th…