Results 21 to 30 of about 924,331 (302)
IEEE Access, 2022 Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks.
Imtiaz Nasim, Ahmed S. Ibrahim
doaj +1 more source
Regularized Transport Between Singular Covariance Matrices [PDF]
IEEE Transactions on Automatic Control, 2021 We consider the problem of steering a linear stochastic system between two end-point degenerate Gaussian distributions in finite time. This accounts for those situations in which some but not all of the state entries are uncertain at the initial, t = 0, and final time, t = T .
Valentina Ciccone +3 more
openaire +4 more sources
Linear Pooling of Sample Covariance Matrices [PDF]
IEEE Transactions on Signal Processing, 2022 We consider the problem of estimating high-dimensional covariance matrices of $K$-populations or classes in the setting where the sample sizes are comparable to the data dimension. We propose estimating each class covariance matrix as a distinct linear combination of all class sample covariance matrices.
Tyler, David E +3 more
openaire +3 more sources
Spiked separable covariance matrices and principal components [PDF]
, 2019 We introduce a class of separable sample covariance matrices of the form $\widetilde{\mathcal{Q}}_1:=\widetilde A^{1/2} X \widetilde B X^* \widetilde A^{1/2}.$ Here $\widetilde{A}$ and $\widetilde{B}$ are positive definite matrices whose spectrums ...
Xiucai Ding, Fan Yang
semanticscholar +1 more source
Local Laws for Sparse Sample Covariance Matrices
Mathematics, 2022 We proved the local Marchenko–Pastur law for sparse sample covariance matrices that corresponded to rectangular observation matrices of order n×m with n/m→y (where y>0) and sparse probability npn>logβn (where β>0).
Alexander N. Tikhomirov +1 more
doaj +1 more source
Nonparametric Estimation of Large Covariance Matrices with Conditional Sparsity
Social Science Research Network, 2020 This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on ...
Hanchao Wang +3 more
semanticscholar +1 more source
Estimating the power spectrum covariance matrix with fewer mock samples [PDF]
, 2015 The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues ...
Pearson, David W., Samushia, Lado
core +2 more sources
Comparing approximate methods for mock catalogues and covariance matrices – I. Correlation function [PDF]
Monthly notices of the Royal Astronomical Society, 2018 This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation
Martha Lippich +21 more
semanticscholar +1 more source
Perturbation theory approach to predict the covariance matrices of the galaxy power spectrum and bispectrum in redshift space [PDF]
, 2019 In this paper, we predict the covariance matrices of both the power spectrum and the bispectrum, including full non-Gaussian contributions, redshift space distortions, linear bias effects and shot-noise corrections, using perturbation theory (PT).
Naonori S. Sugiyama +3 more
semanticscholar +1 more source
Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices [PDF]
Annals of Statistics, 2017 We study the asymptotic distributions of the spiked eigenvalues and the largest nonspiked eigenvalue of the sample covariance matrix under a general covariance matrix model with divergent spiked eigenvalues, while the other eigenvalues are bounded but ...
T. Cai, Xiao Han, G. Pan
semanticscholar +1 more source