Results 51 to 60 of about 556,837 (257)
IEEE Access, 2018 This paper addresses the estimation of large-dimensional covariance matrices under both normal and nonnormal distributions. The shrinkage estimators are constructed by convexly combining the sample covariance matrix and a structured target matrix.
Jianbo Li, Jie Zhou, Bin Zhang
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Choroid plexus volume (CPV) has been proposed as a neuro‐immunological marker of multiple sclerosis (MS), but its relevance in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) remains uncertain. We analyzed CPV in 43 individuals with MOGAD, 48 with MS, and 44 healthy controls using a Bayesian Gaussian mixture modeling ...
Jae‐Won Hyun +4 more
wiley +1 more source
SensorsIn this article, an improved real-valued dimension-reduction MUSIC (IRDR-MUSIC) algorithm is proposed for a crossed-dipole array. Initially, conjugate symmetry of the spatial component in the manifold vector is derived such that two real-valued matrices ...
Hao Nan +3 more
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Assesment of covariance processing with GAIA for nuclear data uncertainty propagation [PDF]
EPJ Web of ConferencesNuclear data uncertainties are provided as covariance matrices in standard nuclear data libraries and propagating them trough neutronics simulations helps quantify the associated uncertainties on the final result. However, processing these matrices often
Sole Pierre +3 more
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Energies, 2021 This paper addresses the implementation and optimization of an Extended Kalman Filter (EKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex-M3 microcontroller.
Justas Dilys +2 more
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Forecasting Covariance Matrices: A Mixed Approach [PDF]
Journal of Financial Econometrics, 2014 In this article, we introduce a new method of forecasting large-dimensional covariance matrices by exploiting the theoretical and empirical potential of mixing forecasts derived from different information sets. The main theoretical contribution of the article is to find the conditions under which a mixed approach (MA) provides a smaller mean squared ...
Halbleib, Roxana, Voev, Valeri
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Relative Efficiency of Maximum Likelihood and Other Estimators in a Nonlinear Regression Model with Small Measurement Errors [PDF]
, 2004 We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error model to the asymptotic covariance matrices of the CS and SQS estimators studied in Kukush et al (2002). For small measurement error variances they are equal
Kukush, Alexander, Schneeweiß, Hans
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Arthritis Care &Research, EarlyView.Objective Hydroxychloroquine (HCQ) is a cornerstone therapy in systemic lupus erythematosus (SLE), but the weight‐based dosing does not account for clinical factors that can introduce individual variability in drug metabolism and clearance. We leveraged longitudinal data from a prospective SLE cohort to identify clinical factors that predict ...
Jay J. Patel +6 more
wiley +1 more source
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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Quadratic shrinkage for large covariance matrices [PDF]
Bernoulli, 2019 This paper constructs a new estimator for large covariance matrices by drawing a bridge between the classic Stein (1975) estimator in finite samples and recent progress under large-dimensional asymptotics. The estimator keeps the eigenvectors of the sample covariance matrix and applies shrinkage to the inverse sample eigenvalues.
Ledoit, Olivier, Wolf, Michael
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