covariance matrices
Generation of 238U Covariance Matrices by Using the Integral Data Assimilation Technique of the CONRAD Code
... Covariance matrices for the neutron cross sections are produced via two types of information: microscopic experiments (such as transmission, capture and fission reaction yields measured in dedicated ...
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Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices
... This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood based estimation. Parametric and nonparamet- ric versions are introduced. Due to ...
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Comparing approximate methods for mock catalogues and covariance matrices - I. Correlation function
... their covariance matrices. In most analyses, covariance ma- trices are computed from a set of mock catalogues designed to reproduce the properties of a given ...the covariance matrix is ...
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Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices
... the covariance matrices from the available data, the source and the noise processes must necessarily be as- sumed ...noise covariance function should be known for any shift and for any angular ...
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Dissecting High-Dimensional Phenotypes with Bayesian Sparse Factor Analysis of Genetic Covariance Matrices
... of covariance matrices does not con- tain matrices that are biologically plausible as a ...of covariance matrices that we search over, a necessary procedure for inference of ...
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Bayesian Semiparametric Modeling of Realized Covariance Matrices
... The iHMM is a nonparametric model that allows the unknown distribution to flexibly change over time. The applications to date assume once a state is entered, the observation is governed by a parametric distribution. This ...
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On the estimation of covariance matrices using panel data artificial regressions
... but covariance matrices calculated using artificial regressions may not be asymptotically valid when the assumption that the error term in the auxiliary model is IID is violated (Davidson and MacKinnon, ...
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Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation
... sample covariance matrix directly for estimating Σ and banding the Cholesky factor matrix for estimating Σ −1 ...sample covariance, which was designed to improve the banding estimator of Bickel and ...for ...
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Group Lasso Estimation of High-dimensional Covariance Matrices
... high-dimensional covariance matrix estimation based on empirical contrast regularization by a group Lasso ...the covariance matrix Σ into a low dimensional space, and thus to a new method of dimension ...
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Causes and effects of negative definite covariance matrices in Swamy Type Random Coefficient Models
... In this paper, we investigate the causes and the finite-sample consequences of negat- ive definite covariance matrices in Swamy type random coefficient models. Monte Carlo experiments reveal that the ...
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Needs of reliable nuclear data and covariance matrices for Burnup Credit in JEFF-3 library
... The use of the Integral Experiment Methodology confirms the good representativity of the MINERVE experiments for BUC industrial application. On the basis of existing best-estimate covariance matrices and ...
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Comparing approximate methods for mock catalogues and covariance matrices II: power spectrum multipoles
... The study of the large-scale structure of the Universe has seen a major step up with the completion of the cosmolog- ical analysis of large galaxy redshift surveys such as SDSS (Tegmark et al. 2004; Eisenstein et al. ...
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Debiasing inference with approximate covariance matrices and other unidentified biases
... We develop our method in section 2. The method is general, but was developed to address currently outstanding problems of cosmic shear. For example, [1, 2] derive the to- date only known completely bias-free likelihood ...
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Improving Bilingual Projections via Sparse Covariance Matrices
... We generate a total of 3000 pairs of points and use 2000 of them for training the models and the rest for evaluation. We use the true feature correspon- dences to form the cross-covariance selection ma- trix I xy ...
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A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
... of covariance and inverse covariance matrix estimation, where often the interest is to estimate a sparse matrix, the proposed method is flexible in estimat- ing both a sparse and well-conditioned ...
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Construction of non-diagonal background error covariance matrices for global chemical data assimilation
... background covariance matrices is challenging due to poorly characterized background errors, and to the very large dimension of the state space of realis- tic atmospheric ...ground covariance ...
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Two Sample Tests for High Dimensional Covariance Matrices
... the covariance matrix is believed to have certain structure, for instance banded or bandable in the sense of Bickel and Levina (2008a), we may modify the test statistic so that the comparison of the two ...
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Strong subadditivity for log determinant of covariance matrices and its applications
... In this Letter we demonstrated that the log-determinant, a simple informational quantity defined on the covariance matrix of any continuous variable state, behaves as a fully fledged entropy, obeying the ...
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The failure of corporate failure models to classify and predict : aspects and refinements
... It suggests that more critical than the failure to meet the conditions of multivariate normality, the equality of the variance-covariance matrices, and the use of a priori probabilities [r] ...
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Three-mode analytical methods for crop improvement programs
... of attribute values for genotype / (/=!,...,#) in environment j (/=! ... mean vectors, covariance matrices and mixing proportions, are estimated using maximum-likelihood methods. In this[r] ...
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