This important study shows a surprising scale-invariance of the covariance spectrum of large-scale recordings in the zebrafish brain in vivo. A convincing analysis demonstrates that a Euclidean random ...

This paper proposes a new covariance modeling technique for Gaussian mixture models. Specifically the inverse covariance (precision) matrix of each Gaussian is ...

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 ...

Spread the loveCovariance is a statistical measurement that evaluates the relationship between two variables. It indicates whether the two variables tend to increase or decrease simultaneously, or if ...

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 ...