Results 1 to 10 of about 3,211,049 (333)
An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter [PDF]
Nonlinear Processes in Geophysics, 2017 The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts.
G. Wu, G. Wu, X. Zheng
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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
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Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance.
P. Li +3 more
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Nonlinear shrinkage estimation of large-dimensional covariance matrices [PDF]
, 2011 Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer ...
Ledoit, Olivier, Wolf, Michael
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
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Asset Allocation Strategies Using Covariance Matrix Estimators
Acta Universitatis Sapientiae: Economics and Business, 2022 The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high ...
László PáL
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DOA-Estimation Method Based on Improved Spatial-Smoothing Technique
Mathematics, 2023 To improve the data utilization of the sensor array and direction-of-arrival-(DOA)-estimation performance for coherent signals, a DOA-estimation method with a modified spatial-smoothing technique is proposed. The covariance matrix of the received data of
Yujun Hou +4 more
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Covariance beamforming, covariance matrix tapers and matrix beamforming are related
Electronics Letters, 2008 It is shown that the covariance beamforming, covariance matrix tapers and matrix beamforming approaches, which were considered separately from one another in the previous array processing literature, are in fact related. The relationships between them in terms of both generality and design procedures are clarified.
J. Li, P. Stoica, T. Yardibi
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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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Electronics Letters, 2021 The existing secret key generation (SKG) techniques are not applicable for frequency division duplex (FDD) Internet of Things networks due to the low power constraints and limited computing resources.
Zheng Wan +3 more
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