Results 51 to 60 of about 4,500,411 (350)
Bayesian Inference for a Covariance Matrix
The Annals of Statistics, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Leonard, Tom, Hsu, John S. J.
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International Journal of Adaptive Control and Signal Processing, 2019 This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems.
Xiao Zhang, F. Ding, Erfu Yang
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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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Analysis of the covariance matrix in FWI through density of covariance maps
CT&F Ciencia, Tecnología & Futuro, 2020 Full waveform inversion (FWI) is a tool for the inversion of seismic data. There are several sources of uncertainty in the results provided by FWI. The quantification of such uncertainties has been studied through the resolution matrix (Res), which rests
Anyeres Neider Jimenez +3 more
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A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems [PDF]
IEEE Transactions on Signal Processing, 2017 A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix ...
A. Aubry, A. de Maio, L. Pallotta
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Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix
Leida xuebao, 2017 In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix.
Yu Han +3 more
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Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data
Separations, 2021 Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features.
Adam Mieldzioc +2 more
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Matrix Completion With Covariate Information
Journal of the American Statistical Association, 2018 This paper investigates the problem of matrix completion from corrupted data, when additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with
Xiaojun Mao +2 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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High‐dimensional covariance matrix estimation
WIREs Computational Statistics, 2019 Covariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics ...
Clifford Lam
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