For decades, quantum computing has felt like something out of science fiction — abstract, theoretical, and always “10 years ...

mx1 = c(1049144,0.6750000,3915, 354464,0.7077872,918, 635201,0.6231416,2892, 8324626,0.6543485,45241, 5155908,0.6935025,24602, 6968511,0.7863728,78734, 986041,0. ...

Abstract: In this article, we address the problem of clutter covariance matrix estimation for radar adaptive detection. Traditional estimation methods are usually based on specific models. However, ...

This important study shows a surprising scale-invariance of the covariance spectrum of large-scale recordings in the zebrafish brain in vivo. A convincing analysis demonstrates that a Euclidean random ...

Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and ...

Abstract: Covariance matrix estimation for a noise-contaminated signal is a common signal processing task, where the covariance matrix of the desired signal together with the noise covariance matrix ...

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant ...

See https://analytic-musings.com/2023/12/30/honey-covariance/ for full writeup. I replicate Wolf & Ledoit's - Honey: I Shrunk the Covariance Matrix (2003) paper ...

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle ...