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Covariance Matrix Estimation for Massive MIMO [PDF]
IEEE Signal Processing Letters, 2018 6 pages, 4 figures.
Vorobyov, Sergiy, A., Upadhya, Karthik
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Automatic positive semidefinate HAC covariance matrix and GMM estimation [PDF]
, 2005 This paper proposes a new class of heteroskedastic and autocorrelation consistent (HAC) covariance matrix estimators. The standard HAC estimation method reweights estimators of the autocovariances.
Smith, Richard J.
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A subspace method for array covariance matrix estimation [PDF]
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016 This paper introduces a subspace method for the estimation of an array covariance matrix. It is shown that when the received signals are uncorrelated, the true array covariance matrices lie in a specific subspace whose dimension is typically much smaller than the dimension of the full space.
Rahmani, Mostafa, Atia, George K.
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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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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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Covariance matrix estimation methods for constrained portfolio optimization in a South African setting [PDF]
, 2010 One of the major topics of concern in Modern Portfolio Theory is portfolio optimization which is centred on the mean-variance framework. In order for this framework to be implemented, esti- mated parameters (covariance matrix for the constrained portfo ...
Madume, Jaison Pezisai
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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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