Results 1 to 10 of about 9,759 (265)
IEEE Access, 2019 Shrinkage regularization is an effective strategy to estimate the covariance matrix of multi-variate random vector in small sample scenarios. The purpose of this paper is to propose improved linear shrinkage estimators of covariance matrix as two types ...
Bin Zhang, Jie Zhou, Jianbo Li
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Estimation of a Covariance Matrix under Stein's Loss
Annals of Statistics, 1985 Stein's general technique for improving upon the best invariant unbiased and minimax estimators of the normal covariance matrix is described. The technique is to obtain solutions to a certain differential inequality involving the eigenvalues of the sample covariance matrix. Several improved estimators are obtained by solving the differential inequality.
Dey, Dipak K., Srinivasan, C.
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Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Entropy, 2022 This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate ...
Bin Zhang, Hengzhen Huang, Jianbin Chen
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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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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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Asset Allocation Strategies Using Covariance Matrix Estimators
Acta Universitatis Sapientiae: Economics and Business, 2022 The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high ...
László PáL
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Estimation of a covariance matrix with zeros [PDF]
Biometrika, 2007 25 ...
Chaudhuri, S. +2 more
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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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Covariance estimation via fiducial inference
Statistical Theory and Related Fields, 2021 As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi +3 more
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Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing
Scientific Reports, 2022 The problem of estimating a large covariance matrix arises in various statistical applications. This paper develops new covariance matrix estimators based on shrinkage regularization.
Bin Zhang, Shoucheng Yuan
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