Results 61 to 70 of about 2,877,467 (378)
Covariance beamforming, covariance matrix tapers and matrix beamforming are related
Electronics Letters, 2008 It is shown that the covariance beamforming, covariance matrix tapers and matrix beamforming approaches, which were considered separately from one another in the previous array processing literature, are in fact related. The relationships between them in terms of both generality and design procedures are clarified.
J. Li, P. Stoica, T. Yardibi
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Moments of minors of Wishart matrices
, 2008 For a random matrix following a Wishart distribution, we derive formulas for the expectation and the covariance matrix of compound matrices. The compound matrix of order $m$ is populated by all $m\times m$-minors of the Wishart matrix.
Drton, Mathias +2 more
core +2 more sources
Covariance Estimation: The GLM and Regularization Perspectives [PDF]
, 2011 Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data ...
Pourahmadi, Mohsen
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Arthritis Care &Research, Accepted Article.Objective Somatic items used in depression assessments can potentially overlap with symptoms related to physical illness, including systemic sclerosis (SSc). No studies have looked at whether somatic depression items may be influenced by diffuse versus limited SSc disease subtypes, which are associated with varying degrees of symptom presentation.
Sophie Hu +109 more
wiley +1 more source
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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IEEE Access, 2019 Reconstructing the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix is a good method for calculating the adaptive beamforming coefficients.
Yuguan Hou +5 more
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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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International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
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
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How Close is the Sample Covariance Matrix to the Actual Covariance Matrix? [PDF]
Journal of Theoretical Probability, 2011 34 pages.
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