Results 11 to 20 of about 402,138 (284)
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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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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The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation
IEEE AccessVarious data analysis techniques and procedures (correlation heatmap, linear discriminant analysis, quadratic discriminant analysis) rely on the estimation of the covariance matrix or its inverse (the precision matrix).
Tuan L. Vo +5 more
doaj +2 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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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
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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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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Estimating the covariance matrix: a new approach [PDF]
Journal of Multivariate Analysis, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tatsuya Kubokawa, M. S. Srivastava
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Bounds for estimation of covariance matrices from heterogeneous samples [PDF]
, 2008 This correspondence derives lower bounds on the mean-square error (MSE) for the estimation of a covariance matrix mbi Mp, using samples mbi Zk,k=1,...,K, whose covariance matrices mbi Mk are randomly distributed around mbi Mp.
Besson, Olivier +2 more
core +2 more sources