We propose a new approach for covariance change point detection applied to graph signals. Specifically, our method draws on the notion of graph stationarity to derive a relevant parameterization of ...

This paper studies the joint limiting behavior of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model, where the asymptotic regime is such that the ...

I replicate the results on modern data: US stock returns from 2005-2022. I then empirically confirm that active weights generated feeding the Ledoit-Wolf shrunk covariance matrix into the optimizer ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

A covariance matrix is a powerful statistical tool that provides insights into the relationships between different variables in a dataset. It indicates the extent to which two or more random variables ...

Chou et al. (2002) have considered the multivariate control chart for monitoring the process mean vector and covariance matrix for the related quality characteristics simultaneously by using ...