ValueError: Expected parameter covariance_matrix (Tensor of shape (4, 4)) of distribution MultivariateNormal (loc: torch.Size ( [4]), covariance_matrix: torch.Size ...

A promising ratio estimator based on singular values of lagged sample auto-covariance matrices has been recently proposed in the literature with a reasonably good performance under some specific ...

Inferring the covariance matrix of multivariate data is of great interest in statistics, finance, and data science. It is often carried out via the maximum likelihood estimation (MLE) principle, which ...

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 ...

Learn how covariance is used to reduce risk in modern portfolio theory, how covariance is calculated and how it is used to provide portfolio diversification.

Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.