Results 11 to 20 of about 547,601 (228)
Automatic Lag Selection in Covariance Matrix Estimation [PDF]
The Review of Economic Studies, 1994 Summary: We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean-squared error ...
Kenneth D. West, Whitney K. Newey
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Sample Space-time Covariance Matrix Estimation [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations.
Delaosa, Connor +4 more
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High‐dimensional covariance matrix estimation [PDF]
WIREs Computational Statistics, 2019 AbstractCovariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of
Clifford Lam
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Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation [PDF]
Econometrica, 1991 This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme.
D. Andrews
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Cholesky-based model averaging for covariance matrix estimation
Statistical Theory and Related Fields, 2017 Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.
Hao Zheng +3 more
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Covariance Matrix Estimation in Massive MIMO [PDF]
IEEE Signal Processing Letters, 2018 submitted to IEEE Signal Processing ...
David Neumann +2 more
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Inertia Estimation Through Covariance Matrix [PDF]
IEEE Transactions on Power Systems, 2022 This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model.
Federico Bizzarri +5 more
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Deep Learning Based Channel Covariance Matrix Estimation With User Location and Scene Images [PDF]
IEEE Transactions on Communications, 2021 Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment ...
Weihua Xu +4 more
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Structured Covariance Matrix Estimation With Missing-(Complex) Data for Radar Applications via Expectation-Maximization [PDF]
IEEE Transactions on Signal Processing, 2021 Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing
A. Aubry +3 more
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Robust low-rank covariance matrix estimation with a general pattern of missing values [PDF]
Signal Processing, 2021 This paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussian assumption, whereas existing robust estimation ones assume unstructured ...
Alexandre Hippert-Ferrer +3 more
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