Results 11 to 20 of about 2,877,467 (378)
Convex Banding of the Covariance Matrix. [PDF]
J Am Stat Assoc, 2016 We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix.
Bien J, Bunea F, Xiao L.
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Covariance Matrix Adaptation MAP-Annealing [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2022 Single-objective optimization algorithms search for the single highest-quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of ...
Matthew C. Fontaine, S. Nikolaidis
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Applied Sciences, 2022 A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this ...
Tuan Tran, Nhat Nguyen, Trung Nguyen
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm.
Tianfu Zhang +5 more
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Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Entropy, 2022 This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate ...
Bin Zhang, Hengzhen Huang, Jianbin Chen
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A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective [PDF]
Nonlinear Processes in Geophysics, 2021 This contribution addresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a ...
O. Pannekoucke +6 more
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Online Covariance Matrix Estimation in Stochastic Gradient Descent [PDF]
Journal of the American Statistical Association, 2020 The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge datasets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and ...
Wanrong Zhu, Xi Chen, W. Wu
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pyGNMF: A Python library for implementation of generalised non-negative matrix factorisation method
SoftwareX, 2022 This article introduces a Python library named pyGNMF, which implements the recently proposed generalised non-negative matrix factorisation (GNMF) method.
Nirav L. Lekinwala, Mani Bhushan
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Covariance matrix adaptation for the rapid illumination of behavior space [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2019 We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diversity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse ...
Matthew C. Fontaine +3 more
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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation [PDF]
Climate of the Past, 2019 Data assimilation has been adapted in paleoclimatology to reconstruct past climate states. A key component of some assimilation systems is the background-error covariance matrix, which controls how the information from observations spreads into the model
V. Valler +5 more
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