Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion [PDF]
Sensors, 2021 Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted ...
Christopher Funk +2 more
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Principal regression for high dimensional covariance matrices. [PDF]
Electron J Stat, 2021 21 pages of main text and references, 3 ...
Zhao Y +3 more
europepmc +5 more sources
Covariance matrices and the separability problem [PDF]
Physical Review Letters, 2006 We propose a unifying approach to the separability problem using covariance matrices of locally measurable observables. From a practical point of view, our approach leads to strong entanglement criteria that allow to detect the entanglement of many bound
J. Eisert +6 more
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Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter [PDF]
Sensors, 2018 Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by
Alexis Nez +4 more
doaj +2 more sources
HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification [PDF]
MethodsX, 2020 We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement. • Compute matrix-
Alexander Litvinenko +4 more
doaj +2 more sources
Noisy Covariance Matrices and Portfolio Optimization [PDF]
The European Physical Journal B - Condensed Matter, 2001 According to recent findings [1,2], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be ...
Crisanti +8 more
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Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices. [PDF]
Probab Theory Relat Fields, 2015 This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai T, Ma Z, Wu Y.
europepmc +4 more sources
A simple procedure for the comparison of covariance matrices [PDF]
BMC Evolutionary Biology, 2012 Background Comparing the covariation patterns of populations or species is a basic step in the evolutionary analysis of quantitative traits. Here I propose a new, simple method to make this comparison in two population samples that is based on comparing ...
Garcia Carlos
doaj +2 more sources
Covariance Shaping Over Riemannian Manifolds for Massive MIMO Communication
IEEE Access, 2023 Acquiring accurate instantaneous channel state information (CSI) is a challenging aspect of massive multi-input multi-output (MIMO) communication. Utilizing statistical information, such as channel covariance matrix, to design statistical beamforming ...
Joarder Jafor Sadique +2 more
doaj +1 more source
Large Dynamic Covariance Matrices [PDF]
Journal of Business & Economic Statistics, 2017 Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ...
Engle, Robert F +2 more
openaire +3 more sources