Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models [PDF]
Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data.
Christopher K. Carter +3 more
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Analysis of brain structural covariance network in Cushing disease
HeliyonBackground: Cushing disease (CD) is a rare clinical neuroendocrine disease. CD is characterized by abnormal hypercortisolism induced by a pituitary adenoma with the secretion of adrenocorticotropic hormone.
Can-Xin Xu +7 more
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GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox
NeuroImageMultimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural ...
Yeongjun Park +11 more
doaj +1 more source
Honey, I shrunk the sample covariance matrix [PDF]
The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer.
Michael Wolf, Olivier Ledoit
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Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix. [PDF]
Visuri et al (2001) proposed and illustrated the use of the affine equivariant rank covariance matrix (RCM) in classical multivariate inference problems.
Croux, Christophe, Ollila, E, Oja, H
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Examination of model uncertainty and parameter sensitivity in correlated systems using covariance structure analysis [PDF]
, 2009 Correlated parameters are often expected when modeling a natural system. However, correlation among the variables often blurs the model uncertainty and makes it difficult to determine parameter sensitivity.
Fan, Cha-Chi
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On the Significance of Covariance for Constraining Theoretical Models from Galaxy Observables
The Astrophysical JournalIn this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite.
Yongseok Jo +3 more
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Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order [PDF]
This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter.
Wouter J. den Haan, Andrew T. Levin
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Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls. [PDF]
Front Hum Neurosci, 2018 Karpati FJ +4 more
europepmc +1 more source