Regularization for high-dimensional covariance matrix by Cui Xiangzhao, Li Chun, Zhao Jine, Zeng Li, Zhang Defei, Pan Jianxin

“…The simulation studies also show that the sample covariance matrix, although performs very badly in covariance estimation for high-dimensional data, can be used to correctly identify the underlying structure of the covariance matrix. …”
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On the Significance of the Quantum Mechanical Covariance Matrix by Avishy Carmi, Eliahu Cohen

“…In this work, we show that the extent of binary correlations in a general class of nonlocal theories can be characterized by the existence of a certain covariance matrix. The set of quantum realizable two-point correlators in the bipartite case then arises from a subtle restriction on the structure of this general covariance matrix. …”
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Covariance matrix filtering with bootstrapped hierarchies. by Christian Bongiorno, Damien Challet

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Identification of processes using recursive covariance matrix / by 368341 Looi, Kwok Onn

“…Dua kaedah penganggaran akandigunakan iaitu kaedah recursive covariance matrix dan recursive least-squares. Contoh penyelesaian beserta analisis dalam pengenalpastian suatusistem akan disertakan untuk memberikan gambaran yang lebih jelas akanperlaksanaan perisian tersebut. …”

Covariance matrix analysis in simultaneous localization and mapping by Nur Aqilah, Othman

“…Nonetheless, EKF-based SLAM suffers from high computational cost due to the update process of covariance matrix. One of the objectives of this thesis is to propose an alternative method to simplify the structure of covariance matrix by means of matrix diagonalization method using eigenvalues. …”
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Bounding entanglement dimensionality from the covariance matrix by Shuheng Liu, Matteo Fadel, Qiongyi He, Marcus Huber, Giuseppe Vitagliano

“…Here, instead, we consider covariances of collective observables, as in the well-known Covariance Matrix Criterion (CMC) \cite{guhnecova} and present a generalization of the CMC for determining the Schmidt number of a bipartite system. …”
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Covariance-Matrix-Based Criteria for Network Entanglement by Kiara Hansenne, Otfried Gühne

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Improving Covariance Matrix Diagonalization in SLAM of Mobile Robot by Maziatun, Mohamad Mazlan, Nur Aqilah, Othman, Hamzah, Ahmad

“…It is evaluated at the updated state covariance matrix of EKF-based SLAM. It is found that the additional of pseudo components in diagonal matrix can improve the covariance matrix and lower the computational complexity. …”
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The Performance Analysis Based on SAR Sample Covariance Matrix by Esra Erten

“…In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. …”
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Econometric Computing with HC and HAC Covariance Matrix Estimators by Achim Zeileis

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Covariance matrix entanglement criterion for an arbitrary set of operators by Vinay Tripathi, Chandrashekar Radhakrishnan, Tim Byrnes

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Covariance Matrix Preparation for Quantum Principal Component Analysis by Max Hunter Gordon, M. Cerezo, Lukasz Cincio, Patrick J. Coles

“…These algorithms assume that the covariance matrix can be encoded in a density matrix, but a concrete protocol for this encoding has been lacking. …”
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Over-sampling imbalanced datasets using the Covariance Matrix by Ireimis Leguen-deVarona, Julio Madera, Yoan Martínez-López, José Hernández-Nieto

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Econometric Computing with HC and HAC Covariance Matrix Estimators by Achim Zeileis

“…Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. …”
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Monitoring the Mean Vector and the Covariance Matrix of Bivariate Processes by Marcela Aparecida Guerreiro Machado, Antônio Fernando Branco Costa

“…This paper proposes the joint use of two charts based on the non-central chi-square statistic (NCS statistic) for monitoring the mean vector and the covariance matrix of bivariate processes, named as the joint NCS charts. …”
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Some Algorithms for the Conditional Mean Vector and Covariance Matrix by John F. Monahan

“…We consider here the problem of computing the mean vector and covariance matrix for a conditional normal distribution, considering especially a sequence of problems where the conditioning variables are changing. …”
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A covariance matrix test for high-dimensional data by Saowapha Chaipitak, Samruam Chongcharoen

“…For the multivariate normally distributed data with the dimension larger than or equal to the number of observations, or the sample size, called high-dimensional normal data, we proposed a test for testing the null hypothesis that the covariance matrix of a normal population is proportional to a given matrix on some conditions when the dimension goes to infinity. …”
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Cholesky-based model averaging for covariance matrix estimation by Hao Zheng, Kam-Wah Tsui, Xiaoning Kang, Xinwei Deng

“…The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables. …”
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Wiener Filter Approximations Without Covariance Matrix Inversion by Pranav U. Damale, Edwin K. P. Chong, Louis L. Scharf

“…Computing the Wiener filter involves the inverse of the observation covariance matrix. In practice, the observation covariance matrix has a large condition number, resulting in unreliable numerical computation of the Wiener filter. …”
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Jackknife covariance matrix estimation for observations from mixture by Rostyslav Maiboroda, Olena Sugakova

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