Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering [PDF]
, 2016 Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different ...
Chang, Jinyuan +3 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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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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Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises
IEEE Access, 2020 This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed.
Gang Hao, Shuli Sun
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Large Dynamic Covariance Matrices [PDF]
SSRN Electronic Journal, 2016 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 ...
Robert F. Engle, Michael Wolf
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M-Matrices as covariance matrices of multinormal distributions
Linear Algebra and its Applications, 1983 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Karlin, Samuel, Rinott, Yosef
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Homogeneity Test of Multi-Sample Covariance Matrices in High Dimensions
Mathematics, 2022 In this paper, a new test statistic based on the weighted Frobenius norm of covariance matrices is proposed to test the homogeneity of multi-group population covariance matrices.
Peng Sun, Yincai Tang, Mingxiang Cao
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Construction of non-diagonal background error covariance matrices for global chemical data assimilation [PDF]
Geoscientific Model Development, 2011 Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere.
K. Singh +5 more
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Transmit Optimization with Improper Gaussian Signaling for Interference Channels [PDF]
, 2013 This paper studies the achievable rates of Gaussian interference channels with additive white Gaussian noise (AWGN), when improper or circularly asymmetric complex Gaussian signaling is applied.
Guan, Yong Liang +4 more
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