To calculate the covariance matrix, you first need to have a dataset with multiple variables. For example, let us consider a dataset with three variables (X, Y, and Z) and five data points: ...

Olivier Ledoit, Michael Wolf, Some Hypothesis Tests for the Covariance Matrix When the Dimension Is Large Compared to the Sample Size, The Annals of Statistics, Vol. 30, No. 4 (Aug., 2002), pp.

Covariance matrix estimation concerns the problem of estimating the covariance matrix from a collection of samples, which is of extreme importance in many applications. Classical results have shown ...

We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterative ...

Abstract: We derive the form of the variance-covariance matrix for any affine equivariant matrix-valued statistics when sampling from complex elliptical distributions. We then use this result to ...

The covariance matrix and correlation matrix are essential tools for understanding the relationships between multiple variables in a dataset. While the covariance matrix provides unnormalized measures ...

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 ...