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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Regularized Tapered Sample Covariance Matrix [PDF]
IEEE Transactions on Signal Processing, 2022 Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer).
Breloy, Arnaud, Ollila, Esa
openaire +5 more sources
Covariate Assisted Principal regression for covariance matrix outcomes. [PDF]
Biostatistics, 2021 AbstractModeling 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 across units that are explained by ...
Zhao Y +4 more
europepmc +4 more sources
The effect on cosmological parameter estimation of a parameter dependent covariance matrix [PDF]
The Open Journal of Astrophysics, 2019 Cosmological large-scale structure analyses based on two-point correlation functions often assume a Gaussian likelihood function with a fixed covariance matrix. We study the impact on cosmological parameter estimation of ignoring the parameter dependence
Darsh Kodwani +2 more
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High‐dimensional covariance matrix estimation [PDF]
WIREs Computational Statistics, 2019 AbstractCovariance 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, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of
Clifford Lam
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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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Longitudinal regression of covariance matrix outcomes. [PDF]
Biostatistics, 2022 SummaryIn this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients,
Zhao Y, Caffo BS, Luo X.
europepmc +4 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
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Perturbative approach to covariance matrix of the matter power spectrum [PDF]
, 2016 We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations.
Mohammed, Irshad +2 more
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Individuals redistribution based on differential evolution for covariance matrix adaptation evolution strategy. [PDF]
Sci Rep, 2022 Chen Z, Liu Y.
europepmc +3 more sources