We consider the problem of recovering random graph signals from nonlinear
measurements. For this setting, closed-form Bayesian estimators are usually intractable and …

In this article, we consider the problem of recovering random graph signals with complex
values. For general Bayesian estimation of complex-valued vectors, it is known that the …

Shrinkage can effectively improve the condition number and accuracy of covariance matrix
estimation, especially for low-sample-support applications with the number of training …

Linear estimation of signals is often based on covariance matrices estimated from training,
which can perform poorly if the training data are limited and the estimated covariance …

The problem of estimating a large covariance matrix arises in various statistical applications.
This paper develops new covariance matrix estimators based on shrinkage regularization …

Precision matrix, ie, inverse covariance matrix, is widely used in signal processing, and often
estimated from training samples. Regularization techniques, such as banding and rank …

Linear signal estimation based on sample covariance matrices (SCMs) can perform poorly if
the training data are limited and the SCMs are ill-conditioned. Diagonal loading (DL) may be …

Conventional superdirective beamforming is a well-known multi-microphone enhancement
method with superior directivity factor (DF). However, it suffers from an inferior white noise …

Estimating the covariance matrix of a random vector is essential and challenging in large
dimension and small sample size scenarios. The purpose of this paper is to produce an …

Linear signal estimators have extensive applications. Under the minimum mean squared
error (MMSE) criterion, the linear MMSE (LMMSE) estimator is optimal but requires …