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: ...

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 ...

The standard method for the comparison of two or more sample covariance matrices is the likelihood ratio test. The purpose of the present paper is to show how this test can be made more informative by ...

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 ...

This paper considers the problem of estimating the bandwidth and the center frequency of a linear chirp signal. The nonstationarity property of chirp signals implies that the signal has high rank and ...

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 ...