Results 11 to 20 of about 9,759 (265)

A Best Linear Empirical Bayes Method for High-Dimensional Covariance Matrix Estimation

SAGE Open, 2023
Covariance matrix estimation plays a significant role in both in the theory and practice of portfolio analysis and risk management. This paper deals with the available data prior to developing a factor model to enhance covariance matrix estimation.
Jin Yuan, Xianghui Yuan
doaj   +1 more source

HighFrequencyCovariance: A Julia Package for Estimating Covariance Matrices Using High Frequency Financial Data

Journal of Statistical Software, 2022
High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialized estimators have been proposed, they have had limited availability in open ...
Stuart Baumann, Margaryta Klymak
doaj   +1 more source

Clutter Covariance Matrix Estimation for Radar Adaptive Detection Based on a Complex-Valued Convolutional Neural Network

Remote Sensing, 2023
In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models ...
Naixin Kang, Zheran Shang, Weijian Liu, Xiaotao Huang   +3 more
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Improved Large Dynamic Covariance Matrix Estimation With Graphical Lasso and Its Application in Portfolio Selection

IEEE Access, 2020
The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However, the
Xin Yuan, Weiqin Yu, Zhixiang Yin, Guoqiang Wang   +3 more
doaj   +1 more source

Covariance Matrix Estimation for Massive MIMO [PDF]

IEEE Signal Processing Letters, 2018
6 pages, 4 figures.
Vorobyov, Sergiy, A., Upadhya, Karthik
openaire   +3 more sources

Estimation of the Parameters of Power Function Distribution based on Progressively Type-II Right Censoring with Binomial Removal

Statistica, 2023
In this article, we proposed the estimates of unknown parameters of power function distribution in the context of progressive type-II censoring with binomial removals, where the number of units removed at each failure time follows a binomial distribution.
E.I. Abdul Sathar, G.S. Sathyareji
doaj   +1 more source

Covariance Matrix Estimation in Massive MIMO [PDF]

IEEE Signal Processing Letters, 2018
submitted to IEEE Signal Processing ...
David Neumann, Michael Joham, Wolfgang Utschick   +2 more
openaire   +2 more sources

Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection

Sensors, 2019
This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance
Naixin Kang, Zheran Shang, Qinglei Du
doaj   +1 more source

Econometric Computing with HC and HAC Covariance Matrix Estimators

Journal of Statistical Software, 2004
Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the ...
Achim Zeileis
doaj   +3 more sources

Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data

Separations, 2021
Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features.
Adam Mieldzioc, Monika Mokrzycka, Aneta Sawikowska   +2 more
doaj   +1 more source