A positive definite estimator of a covariance matrix with zero entries provides a valid covariance matrix that can be used an input in almost any area of ...

The critical case, where a principal minor is zero, leads to define a new lossless lattice structure with zeros on the unit circle, also useful in the standard ...

It has been shown in [15] that the generalized thresholding estima- tors are consistent over a large class of (approximately) sparse covariance matrices.

The proposed covariance estimator, the soft thresholding covariance estimator, and a diagonal estimator S+ were used in place of E i and ¿2 . Tuning parameters ...

In our implementation, we use the soft-thresholding estimator as the initial value for both 0 and 0, and we set 0 as a zero matrix. Note that our convergence ...

This paper considers regularizing a covariance matrix of p vari- ables estimated from n observations, by hard thresholding. We show.

Jun 11, 2023 · Abstract. Repeated measurements are common in many fields, where random variables are ob- served repeatedly across different subjects.

Abstract. This paper deals with the estimation of a high-dimensional covariance with a con- ditional sparsity structure and fast-diverging eigenvalues.

Abstract: We propose a new approach for estimating high dimensional positive-definite covariance matrices. Our method extends the generalized thresholding ...

Jun 14, 2023 · Here is a way with the CVXR package. It does not allow to impose on a variable matrix to be positive definite, only positive semidefinite.