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
doaj +1 more source
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
core +2 more sources
Shrinkage Estimation of Large Covariance Matrices: Keep it Simple, Statistician?
Journal of Multivariate Analysis, 2020 Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix.
Olivier Ledoit, Michael Wolf
semanticscholar +1 more source
Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices [PDF]
Journal of the American Statistical Association, 2018 This article considers the problem of estimating a change point in the covariance matrix in a sequence of high-dimensional vectors, where the dimension is substantially larger than the sample size. A two-stage approach is proposed to efficiently estimate
H. Dette, G. Pan, Qing Yang
semanticscholar +1 more source
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
doaj +1 more source
High-dimensional covariance matrices in elliptical distributions with application to spherical test [PDF]
Annals of Statistics, 2018 This paper discusses fluctuations of linear spectral statistics of high-dimensional sample covariance matrices when the underlying population follows an elliptical distribution.
Jiang Hu, Weiming Li, Zhi Liu, Wang Zhou
semanticscholar +1 more source
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
doaj +1 more source
Edge universality of separable covariance matrices [PDF]
Electronic Journal of Probability, 2018 In this paper, we prove the edge universality of largest eigenvalues for separable covariance matrices of the form $\mathcal Q :=A^{1/2}XBX^*A^{1/2}$. Here $X=(x_{ij})$ is an $n\times N$ random matrix with $x_{ij}=N^{-1/2}q_{ij}$, where $q_{ij}$ are $i.i.
Fan Yang
semanticscholar +1 more source
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
doaj +1 more source
The Graphical Horseshoe Estimator for Inverse Covariance Matrices [PDF]
Journal of Computational And Graphical Statistics, 2017 We develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data using the horseshoe prior. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators, such as the ...
Yunfan Li, B. Craig, A. Bhadra
semanticscholar +1 more source