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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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
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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
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ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES. [PDF]
Ann Stat, 2015 Published at http://dx.doi.org/10.1214/15-AOS1357 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Fan J, Rigollet P, Wang W.
europepmc +7 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
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Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices. [PDF]
Bayesian Anal, 2020 Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a ...
Lan S +5 more
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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
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Linear Pooling of Sample Covariance Matrices [PDF]
IEEE Transactions on Signal Processing, 2022 We consider the problem of estimating high-dimensional covariance matrices of $K$-populations or classes in the setting where the sample sizes are comparable to the data dimension. We propose estimating each class covariance matrix as a distinct linear combination of all class sample covariance matrices.
Tyler, David E +3 more
openaire +3 more sources
Shrinkage Estimators for Covariance Matrices [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels, Michael J., Kass, Robert E.
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Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations
IEEE Access, 2020 Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise ...
Yanmin Zhu +3 more
doaj +1 more source