Results 51 to 60 of about 192,768 (193)
Direct Nonlinear Shrinkage Estimation of Large-Dimensional Covariance Matrices [PDF]
SSRN Electronic Journal, 2017 This paper introduces a nonlinear shrinkage estimator of the covariance matrix that does not require recovering the population eigenvalues first. We estimate the sample spectral density and its Hilbert transform directly by smoothing the sample eigenvalues with a variable-bandwidth kernel. Relative to numerically inverting the so-called QuEST function,
Ledoit, Olivier, Wolf, Michael
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Performance of penalized maximum likelihood in estimation of genetic covariances matrices
Genetics Selection Evolution, 2011 Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of
Meyer Karin
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A data driven equivariant approach to constrained Gaussian mixture modeling
, 2016 Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods
Di Mari, Roberto +2 more
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Robust estimation of high-dimensional covariance and precision matrices [PDF]
Biometrika, 2018 High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption.
Avella, M, Battey, HS, Fan, J, Li, Q
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Element Aggregation for Estimation of High-Dimensional Covariance Matrices
MathematicsThis study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors.
Jingying Yang
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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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Penalized maximum likelihood for multivariate Gaussian mixture
, 2001 In this paper, we first consider the parameter estimation of a multivariate random process distribution using multivariate Gaussian mixture law. The labels of the mixture are allowed to have a general probability law which gives the possibility to ...
Mohammad-Djafari, Ali, Snoussi, Hichem
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Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings [PDF]
, 2014 Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a high-dimensional covariance matrix are often employed to circumvent the ...
Touloumis, Anestis
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Methodology, 2020 Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants.
Eunkyeng Baek, John J. M. Ferron
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Regularized Block Toeplitz Covariance Matrix Estimation via Kronecker Product Expansions [PDF]
, 2014 In this work we consider the estimation of spatio-temporal covariance matrices in the low sample non-Gaussian regime. We impose covariance structure in the form of a sum of Kronecker products decomposition (Tsiligkaridis et al.
Greenewald, Kristjan +1 more
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