Results 41 to 50 of about 134,557 (305)
On the maximum of covariance estimators
Journal of Multivariate Analysis, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Estimating Covariance Matrices
The Annals of Statistics, 1991 Let \(S_ 1\sim W_ p(\Sigma_ 1,n_ 1)\) and \(S_ 2\sim W_ p(\Sigma_ 2,n_ 2)\) be two independent \(p\times p\) Wishart matrices. It is desired to consider the minimax estimation of \((\Sigma_ 1,\Sigma_ 2)\) under the loss function \[ \sum_{i=1}^ 2\{\hbox {tr}(\Sigma_ i^{-1}\hat\Sigma_ i-\log| \Sigma_ i^{- 1}\hat\Sigma_ i|-p\}, \] extending known results ...
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Study of harmonics detection based on parametric spectral estimation method
Gong-kuang zidonghua, 2016 Three parametric spectral estimation methods including Yule Walker, Burg and Covariance were studied and an improved Covariance method was proposed based on analysis of AR model.
ZHANG Tingzhong +3 more
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Best linear unbiased estimation for varying probability with and without replacement sampling
Special Matrices, 2019 When sample survey data with complex design (stratification, clustering, unequal selection or inclusion probabilities, and weighting) are used for linear models, estimation of model parameters and their covariance matrices becomes complicated.
Haslett Stephen
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Channel Covariance Identification in FDD Massive MIMO Systems
Proceedings, 2018 Channel estimation for Massive MIMO systems has drawn a lot of attention in the last years. A number of estimation methods rely on the knowledge of the channel covariance matrix to operate. However, this covariance is not known in practice, and it should
José P. González-Coma +3 more
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Estimating cosmological parameter covariance [PDF]
Monthly Notices of the Royal Astronomical Society, 2014 We investigate the bias and error in estimates of the cosmological parameter covariance matrix, due to sampling or modelling the data covariance matrix, for likelihood width and peak scatter estimators. We show that these estimators do not coincide unless the data covariance is exactly known. For sampled data covariances, with Gaussian distributed data
Taylor, Andy, Joachimi, Benjamin
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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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Covariance structure estimation with Laplace approximation
Journal of Multivariate Analysis, 2023 Gaussian covariance graph model is a popular model in revealing underlying dependency structures among random variables. A Bayesian approach to the estimation of covariance structures uses priors that force zeros on some off-diagonal entries of covariance matrices and put a positive definite constraint on matrices.
Bongjung Sung, Jaeyong Lee
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FEBS Open Bio, EarlyView.The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
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IEEE Access, 2020 The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However, the
Xin Yuan +3 more
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