Results 1 to 10 of about 55,665 (307)
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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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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On confidence intervals for precision matrices and the eigendecomposition of covariance matrices
CoRR, 2022 The eigendecomposition of a matrix is the central procedure in probabilistic models based on matrix factorization, for instance principal component analysis and topic models. Quantifying the uncertainty of such a decomposition based on a finite sample estimate is essential to reasoning under uncertainty when employing such models.
Teodora Popordanoska +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingAccurate error covariance is crucial for postprocessing gravity recovery and climate experiment (GRACE) gravity field solutions in terms of spherical harmonic coefficients (SHCs).
Lin Zhang +3 more
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Patch-Based Principal Covariance Discriminative Learning for Image Set Classification
IEEE Access, 2017 Image set classification has attracted increasing attention with respect to the use of significant amounts of within-set information. The covariance matrix is a natural and effective descriptor for describing image sets. Non-singular covariance matrices,
Hengliang Tan, Ying Gao
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Estimating Covariance Matrices
The Annals of Statistics, 1991 Let \(S_ 1\sim W_ p(\Sigma_ 1,n_ 1)\) and \(S_ 2\sim W_ p(\Sigma_ 2,n_ 2)\) be two independent \(p\times p\) Wishart matrices. It is desired to consider the minimax estimation of \((\Sigma_ 1,\Sigma_ 2)\) under the loss function \[ \sum_{i=1}^ 2\{\hbox {tr}(\Sigma_ i^{-1}\hat\Sigma_ i-\log| \Sigma_ i^{- 1}\hat\Sigma_ i|-p\}, \] extending known results ...
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A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance
Sensors, 2018 The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system.
Binqi Zheng +3 more
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Proportionality of Covariance Matrices
The Annals of Statistics, 1987 S\({}_ 0,S_ 1,...,S_ k\) are mutually independent p by p matrices, \(S_ i\) having a Wishart distribution with \(n_ i\) degrees of freedom and expectation \(\Sigma_ i\). The likelihood ratio test of the hypothesis \(\Sigma_ i=\lambda_ i\Sigma_ 0\) for \(i=1,...,k\) is developed.
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