Results 11 to 20 of about 108,134 (338)
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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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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Journal of Statistical Software, 2022 High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialized estimators have been proposed, they have had limited availability in open ...
Stuart Baumann, Margaryta Klymak
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A Best Linear Empirical Bayes Method for High-Dimensional Covariance Matrix Estimation
SAGE Open, 2023 Covariance matrix estimation plays a significant role in both in the theory and practice of portfolio analysis and risk management. This paper deals with the available data prior to developing a factor model to enhance covariance matrix estimation.
Jin Yuan, Xianghui Yuan
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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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Remote Sensing, 2023 In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models ...
Naixin Kang +3 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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IEEE Access, 2020 The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However, the
Xin Yuan +3 more
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Statistica, 2023 In this article, we proposed the estimates of unknown parameters of power function distribution in the context of progressive type-II censoring with binomial removals, where the number of units removed at each failure time follows a binomial distribution.
E.I. Abdul Sathar, G.S. Sathyareji
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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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