Results 1 to 10 of about 547,601 (228)
Covariance matrix estimation with heterogeneous samples [PDF]
IEEE Transactions on Signal Processing, 2008 We consider the problem of estimating the covariance matrix Mp of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly Mp.
Besson, Olivier +2 more
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
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Estimation of a Covariance Matrix with Zeros [PDF]
Biometrika, 2005 We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call Iterative Conditional Fitting, for computing the maximum likelihood ...
Chaudhuri, Sanjay +2 more
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Sparse Covariance Matrix Estimation With Eigenvalue Constraints. [PDF]
J Comput Graph Stat, 2014 We propose a new approach for estimating high-dimensional, positive-definite covariance matrices. Our method extends the generalized thresholding operator by adding an explicit eigenvalue constraint. The estimated covariance matrix simultaneously achieves sparsity and positive definiteness.
Liu H, Wang L, Zhao T.
europepmc +4 more sources
Covariance Matrix Estimation for Massive MIMO [PDF]
IEEE Signal Processing Letters, 2018 We propose a novel pilot structure for covariance matrix estimation in massive multiple-input multiple-output (MIMO) systems in which each user transmits two pilot sequences, with the second pilot sequence multiplied by a random phase-shift.
Upadhya, Karthik, Vorobyov, Sergiy A.
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A nonparametric empirical Bayes approach to covariance matrix estimation
Biometrics, 2021 We propose an empirical Bayes method to estimate high-dimensional covariance matrices. Our procedure centers on vectorizing the covariance matrix and treating matrix estimation as a vector estimation problem.
Xin, Huiqin, Zhao, Sihai Dave
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm.
Tianfu Zhang +5 more
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Applied Sciences, 2022 A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this ...
Tuan Tran, Nhat Nguyen, Trung Nguyen
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2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion
Sensors, 2022 In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains.
Ruru Mei +3 more
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Sparse estimation of a covariance matrix [PDF]
Biometrika, 2011 We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even ...
Jacob Bien, Robert J. Tibshirani
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