Results 1 to 10 of about 140,209 (263)
Covariate Assisted Principal regression for covariance matrix outcomes. [PDF]
Biostatistics, 2021 Abstract Modeling variances in data has been an important topic in many fields, including in financial and neuroimaging analysis. We consider the problem of regressing covariance matrices on a vector covariates, collected from each observational unit. The main aim is to uncover the variation in the covariance matrices
Zhao Y +4 more
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Covariance matrix filtering with bootstrapped hierarchies. [PDF]
PLoS ONE, 2021 Cleaning covariance matrices is a highly non-trivial problem, yet of central importance in the statistical inference of dependence between objects. We propose here a probabilistic hierarchical clustering method, named Bootstrapped Average Hierarchical ...
Christian Bongiorno, Damien Challet
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Covariance-Matrix-Based Criteria for Network Entanglement [PDF]
Entropy, 2023 Quantum networks offer a realistic and practical scheme for generating multiparticle entanglement and implementing multiparticle quantum communication protocols.
Kiara Hansenne, Otfried Gühne
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Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network [PDF]
SensorsThis work investigates spectrum sensing in cognitive radio networks, where multi-antenna secondary users aim to detect the spectral occupancy of noncircular signals transmitted by primary users.
Songlin Chen +3 more
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Convex Banding of the Covariance Matrix. [PDF]
J Am Stat Assoc, 2016 We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix.
Bien J, Bunea F, Xiao L.
europepmc +5 more sources
Applied Sciences, 2022 A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this ...
Tuan Tran, Nhat Nguyen, Trung Nguyen
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm.
Tianfu Zhang +5 more
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Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Entropy, 2022 This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate ...
Bin Zhang, Hengzhen Huang, Jianbin Chen
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A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective [PDF]
Nonlinear Processes in Geophysics, 2021 This contribution addresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a ...
O. Pannekoucke +6 more
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Covariance Matrix Estimation in Complex Surveys [PDF]
The Egyptian Statistical Journal, 1989 An estimator of asymptotic covariance matrix of vector of second-order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
Muhammad Pervaiz
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