Results 21 to 30 of about 556,837 (257)

M-Matrices as covariance matrices of multinormal distributions

Linear Algebra and its Applications, 1983
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Karlin, Samuel, Rinott, Yosef
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Nonparametric estimation of covariance functions by model selection [PDF]

, 2009
We propose a model selection approach for covariance estimation of a stochastic process. Under very general assumptions, observing i.i.d replications of the process at fixed observation points, we construct an estimator of the covariance function by ...
Muniz Alvarez, Lilian, Muñiz-Alvarez, Lilian, Biscay, Rolando, Lilian, Muniz-Alvarez, Jérémie Bigot, Lilian Muñiz-Alvarez, Bigot, Jérémie, Rolando Biscay, Jean-Michel Loubes, Loubes, Jean-Michel   +9 more
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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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Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

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, Laetitia Fradet, Frédéric Marin, Tony Monnet, Patrick Lacouture   +4 more
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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, M. Jardak, A. Sandu, K. Bowman, M. Lee, D. Jones   +5 more
doaj   +1 more source

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, Aleksei Tiulpin, Wacha Bounliphone, Matthew B. Blaschko   +3 more
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Robust covariance estimation for data fusion from multiple sensors [PDF]

, 2011
This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics.
Lazarus, Samuel B., Samuel B. Lazarus, Tsourdos, Antonios, Antonios Tsourdos, João Sequeira, Sequeira, João   +5 more
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Covariance Matrix Reconstruction of GRACE Monthly Solutions Using Common Factors and Individual Formal Errors

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Accurate error covariance is crucial for postprocessing gravity recovery and climate experiment (GRACE) gravity field solutions in terms of spherical harmonic coefficients (SHCs).
Lin Zhang, Yunzhong Shen, Kunpu Ji, Qiujie Chen   +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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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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