Results 21 to 30 of about 82,988 (261)
Random matrix-improved estimation of covariance matrix distances [PDF]
Journal of Multivariate Analysis, 2019 Given two sets $x_1^{(1)},\ldots,x_{n_1}^{(1)}$ and $x_1^{(2)},\ldots,x_{n_2}^{(2)}\in\mathbb{R}^p$ (or $\mathbb{C}^p$) of random vectors with zero mean and positive definite covariance matrices $C_1$ and $C_2\in\mathbb{R}^{p\times p}$ (or $\mathbb{C}^{p\times p}$), respectively, this article provides novel estimators for a wide range of distances ...
Couillet, Romain +3 more
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A Compound Decision Approach to Covariance Matrix Estimation
Biometrics, 2022 AbstractCovariance matrix estimation is a fundamental statistical task in many applications, but the sample covariance matrix is suboptimal when the sample size is comparable to or less than the number of features. Such high-dimensional settings are common in modern genomics, where covariance matrix estimation is frequently employed as a method for ...
Huiqin Xin, Sihai Dave Zhao
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Cholesky-based model averaging for covariance matrix estimation
Statistical Theory and Related Fields, 2017 Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.
Hao Zheng +3 more
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Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors [PDF]
مدیریت مهندسی و رایانش نرم, 2023 Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference.
Saman Rezaeizadeh, Mehdi Bekrani
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High‐dimensional covariance matrix estimation [PDF]
WIREs Computational Statistics, 2019 AbstractCovariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of
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New properties for Tyler's covariance matrix estimator [PDF]
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016 In this paper, we deal with covariance matrix estimation in complex elliptically symmetric (CES) distributions. We focus on Tyler's estimator (TyE) and the well-known sample covariance matrix (SCM). TyE is widely used in practice, but its statistical behavior is still poorly understood.
Draskovic, Gordana, Pascal, Frédéric
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Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Journal of Electrical and Computer Engineering, 2012 Accurate covariance matrix estimation for high-dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image ...
Nir Gorelik +3 more
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Analysis of Semi-Blind Channel Estimation in Multiuser Massive MIMO Systems With Perturbations
IEEE Access, 2019 In the massive multiple-input multiple-output (MIMO) systems, pilot contamination and signal perturbation are two important issues in the semi-blind channel estimation methods.
Cheng Hu, Hong Wang, Rongfang Song
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IET Radar, Sonar & Navigation, 2021 To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN)
Hao Xiao, Tong Wang, Cai Wen, Bing Ren
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Sample Space-time Covariance Matrix Estimation [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations.
Delaosa, Connor +4 more
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