covariance matrix estimation
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
... (inverse) covariance matrix estimation is becoming more and more common in various scientific and technological ...the covariance matrix, and based on banding or thresholding the sample ...
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A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar
... persymmetry covariance matrix estimation in the airborne MIMO radar is ...clutter covariance matrix is estimated by the original data and the constructed ...
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Sample space-time covariance matrix estimation
... R(z) = U(z)Λ(z)U P (z) , (2) where U(z) is a paraunitary matrix of eigenvectors and Λ(z) is a diagonal parahermitian matrix of eigenvalues. In most standard cases, these factors can be selected to be ...
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Band Width Selection for High Dimensional Covariance Matrix Estimation
... The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti- mators. Both estimators require a band width parameter. We ...
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A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
... Much of the recent literature focuses on two broad classes of regularized covariance matrix estimation. i) The one class relies on natural ordering among variables, where one often assumes that the ...
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Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes
... sample covariance matrix (SCM) even in compound-Gaussian ...sample matrix is the maximum likeli- hood estimate (MLE) of the covariance matrix for Gaus- sian disturbance, but is no ...
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Group Lasso Estimation of High-dimensional Covariance Matrices
... a matrix regression model as in Bigot et ...high-dimensional covariance matrix estimation based on empirical contrast regularization by a group Lasso ...the covariance matrix Σ ...
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CS-SFD Algorithm for GNSS Anti-Jamming Receivers
... space-time covariance matrix is perfectly ...the covariance matrix in the case of fewer snapshots, which may lead to remarkable performance ...interference covariance matrix ...
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Eigenvalue regularized covariance matrix estimators for high dimensional data
... a covariance matrix or its inverse, called the precision matrix, is an important and sometimes inevitable task in data ...risk estimation and portfolio allocation in finance to classification ...
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Support estimation of a sample space-time covariance matrix
... space-time covariance matrix estimation, this paper has presented an empirical approach to the estimation of the support for such ...space-time covariance matrix, on which an ...
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Autocorrelation based factor analysis and nonlinear shrinkage estimation of large integrated covariance matrix
... integrated covariance matrix ...realized covariance matrix still suffers from bias contributed from the extreme eigenvalues when the number of assets is ...volatility matrix which ...
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K-MEANS Clustering with a Covariance Matrix
... on covariance matrix is ...inducing matrix so that the clusters with dissimilar structres that inherent in a data set can be ...singularity matrix or by considering linearly correlated or ...
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An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application
... where the low rank structure emerges as a consequence of a few factors driving the data matrix. For example, in Fama-French factor models, the excess returns in a stock market are driven by a few common factors ...
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On errors in variables estimation with unknown noise variance ratio
... noise covariance structure, i.e., the covariance matrix of vec( D) ˜ is known up to a scaling ...noise covariance structure up to a scalar is again restrictive in ...
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Learning the Structure of Linear Latent Variable Models
... The traditional framework for discovering latent variables is factor analysis and its variants (see, e.g., Bartholomew et al., 2002). A number of factors is chosen based on some criterion such as the minimum number of ...
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Weak factor model in large dimension
... Another related area of research with weak factors is the multi-level factor model, which usually includes global and regional levels. In such model, the factors are separated into levels, where the top level factors ...
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Kernel Density Estimation for the Eigenvalues of Variance Covariance Matrix of FFT Scaling of DNA Sequences: An Empirical Study of Some Organisms
... density estimation for the first, second, third and fourth eigenvalues of variance covariance matrix of Fast Fourier Transform (FFT) for numerical values representation of DNA sequences of five ...
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systemfit: A Package to Estimate Simultaneous Equation Systems in R
... the covariance structure of the residuals into account, leads to efficient ...This estimation procedure is generally called “Seemingly Unrelated Regression” (SUR) (Zellner, ...
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Nonparametric estimation of time varying covariance matrix in a slowly changing vector random walk model
... the estimation of conditional covariance matrix, little research on the estimation of local covariance matrix can be found in ...volatility estimation under a ...
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APPROXIMATION TO THE COVARIANCE MATRIX FOR STOCHASTIC POINT KINETICS
... In this work, the square root of the matrix of variances in stochastic point kinetics is analytically deduced using Cholesky decomposition. The system under study is solved numerically using the implicit Milstein ...
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