Results 61 to 70 of about 1,787,003 (299)
Covariance structure estimation with Laplace approximation
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
openaire +2 more sources
Stage‐Dependent β‐Synuclein Links MRI and Cognitive Decline in Alzheimer's Disease
ABSTRACT Objective Synaptic degeneration drives cognitive decline in Alzheimer's disease (AD), but synaptic biomarkers are scarce. Brain‐enriched β‐synuclein emerged as a synaptic damage marker. We investigated its diagnostic, prognostic, and structural correlates across the AD continuum.
Ulaş Ay +15 more
wiley +1 more source
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
Adaptive covariance estimation with model selection [PDF]
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator.
Biscay, Rolando +2 more
openaire +3 more sources
ABSTRACT Objective Amyotrophic lateral sclerosis (ALS) is a clinically heterogeneous neurodegenerative disease requiring reliable biomarkers to improve patient stratification and trial design. While serum neurofilament light chain (sNfL) reflects neuroaxonal stress and disease aggressiveness, troponin T (TnT) may capture complementary aspects of ...
Julia Sellin +8 more
wiley +1 more source
A Cascaded Neural Network for Robust Phase-Only Beamforming Under Covariance Matrix Mismatch
This paper presents a cascaded neural network framework for phase-only beamforming under covariance matrix mismatch. The proposed architecture combines a denoising autoencoder (DAE) with a residual network (ResNet) to address performance degradation ...
Zhonghui Zhao +5 more
doaj +1 more source
Safety and Efficacy of GLP‐1 Receptor Agonists in Adults With Epilepsy, Obesity, and Type 2 Diabetes
ABSTRACT Objective Managing obesity in patients with epilepsy is complicated by the weight‐gaining properties of essential antiseizure medications (ASMs) such as valproate and pregabalin. We evaluated the safety and efficacy of initiating glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) in this population.
Hyoshin Son +3 more
wiley +1 more source
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
Cholesky-based model averaging for covariance matrix estimation
Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.
Hao Zheng +3 more
doaj +1 more source
Regularization for high-dimensional covariance matrix
In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n.
Cui Xiangzhao +5 more
doaj +1 more source

