Results 141 to 150 of about 4,500,411 (350)
Shrinkage regularization is an effective strategy to estimate the covariance matrix of multi-variate random vector in small sample scenarios. The purpose of this paper is to propose improved linear shrinkage estimators of covariance matrix as two types ...
Bin Zhang, Jie Zhou, Jianbo Li
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
core
Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and ...
P. Ciais +32 more
wiley +1 more source
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
core
Mice can transfer the learned rule of spatial working memory to guide similar but novel tasks. Hippocampal CA3 populational activity dynamically reorganize during memory generalization, shifting from task‐specific to generalized coding over testing days. Sparse yet redundant neural representations of CA3 enable rule transfer and cognitive map formation,
Da Song +8 more
wiley +1 more source
Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods
Dingyi Zhou, Zhifang Zhao
doaj +1 more source
Estimating High Dimensional Covariance Matrices and its Applications [PDF]
Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater ...
Jushan Bai, Shuzhong Shi
core
A Panel of Circulating Exosomal sncRNAs Associated With Lung Cancer Risk up to 10 Years in Advance
Lung cancer is often diagnosed too late, and current screening overlooks many people at risk. In a long‐term study of smokers, a panel of small non‐coding RNAs carried in blood exosomes signals elevated lung cancer risk up to ten years before diagnosis, pointing toward a blood‐based tool for earlier risk detection.
Zhuokun Feng +12 more
wiley +1 more source
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
core
A conformal ALD‐IGZO vertical RRAM integrates volatile and nonvolatile switching within a compact 2F architecture. Before forming, tunable volatile dynamics provide fading‐memory reservoir states, while after forming, stable multilevel conductance modulation enables hardware readout.
Seeun Lee +6 more
wiley +1 more source

