Results 1 to 10 of about 615,565 (289)
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
Covariance matrix estimation with heterogeneous samples [PDF]
IEEE Transactions on Signal Processing, 2008 We consider the problem of estimating the covariance matrix Mp of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly Mp.
Besson, Olivier +2 more
core +5 more sources
Estimation of a Covariance Matrix with Zeros [PDF]
Biometrika, 2005 We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call Iterative Conditional Fitting, for computing the maximum likelihood ...
Chaudhuri, Sanjay +2 more
core +3 more sources
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
europepmc +4 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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source