Results 21 to 30 of about 4,500,411 (350)
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, Wei-Biao Wu
semanticscholar +1 more source
Covariance Matrix Estimation in Complex Surveys [PDF]
The Egyptian Statistical Journal, 1989 An estimator of asymptotic covariance matrix of vector of second-order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
Muhammad Pervaiz
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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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Sparse estimation of a covariance matrix [PDF]
Biometrika, 2011 We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even ...
Jacob Bien, Robert J. Tibshirani
openaire +3 more sources
The effect on cosmological parameter estimation of a parameter dependent covariance matrix
The Open Journal of Astrophysics, 2019 Cosmological large-scale structure analyses based on two-point correlation functions often assume a Gaussian likelihood function with a fixed covariance matrix. We study the impact on cosmological parameter estimation of ignoring the parameter dependence
Darsh Kodwani +2 more
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The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation
Journal of Financial Econometrics, 2020 Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz’s portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a ...
Olivier Ledoit, Michael Wolf
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