Results 41 to 50 of about 531,665 (271)
Covariance Estimation in High Dimensions via Kronecker Product Expansions [PDF]
, 2013 This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d.
Alfred O. Hero Iii +2 more
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Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
Frontiers in Plant Science, 2020 Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data ...
Deniz Akdemir +3 more
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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 ...
openaire +2 more sources
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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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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Evaluation of structure specification in linear mixed models for modeling the spatial effects in tree height-diamater relationships [PDF]
Annals of Forest Research, 2013 In recent years, linear mixed models (LMM) have become more popularto deal with spatial effects in forestry and ecological data. In this study, different structure specifications of linear mixed model were applied to model tree height-diameter ...
Junfeng Lu, Lianjun Zhang
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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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Sparse permutation invariant covariance estimation
, 2007 The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lasso-type penalty ...
Bickel, Peter J. +3 more
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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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Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes [PDF]
, 2013 Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter.
Bachoc, François
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