Results 11 to 20 of about 623,991 (302)
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
europepmc +2 more sources
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
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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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Covariance Matrices and the Separability Problem [PDF]
Physical Review Letters, 2007 4 pages, no figures; v3: final version to appear in ...
Gühne, O. +3 more
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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 heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ...
Engle, Robert F +2 more
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A Novel Adaptive Kalman Filter With Colored Measurement Noise
IEEE Access, 2018 In this paper, a novel variational Bayesian-based adaptive Kalman filter (VBAKF) is proposed to solve the problem of a linear state-space model with colored measurement noise and inaccurate noise covariance matrices.
Yonggang Zhang +3 more
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Estimation Method of Covariance Matrix in Atmospheric Inversion of CO2 Emissions [PDF]
E3S Web of ConferencesAtmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux.
Han Yubin +4 more
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Bayesian Variance-Covariance Matrices and Selection Index [PDF]
The Egyptian Statistical Journal, 1988 Bayesian estimates of variance-covariance matrix have been obtained. Two loss functions were used to estimate five Bayesian matrices of variance-covariance matrix S.
A. Al Sobayel, A. Ali
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A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
Applied Sciences, 2021 In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the
Ping Dong, Jianhua Cheng, Liqiang Liu
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Covariance matrix estimation with heterogeneous samples [PDF]
, 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.
Bidon, Stéphanie +2 more
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