Results 81 to 90 of about 2,877,467 (378)
Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique
Remote Sensing, 2022 The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D ...
Jiandong Qiao +4 more
doaj +1 more source
Optimal Estimation of a Large-Dimensional Covariance Matrix Under Stein's Loss
Bernoulli, 2017 This paper introduces a new method for deriving covariance matrix estimators that are decision-theoretically optimal within a class of nonlinear shrinkage estimators. The key is to employ large-dimensional asymptotics: the matrix dimension and the sample
Olivier Ledoit, Michael Wolf
semanticscholar +1 more source
Advanced Materials, EarlyView.Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje +7 more
wiley +1 more source
Some quantitative characteristics of error covariance for Kalman filters
Tellus: Series A, Dynamic Meteorology and Oceanography, 2021 Some quantitative characteristics of error covariance are studied for linear Kalman filters. These quantitative characteristics include the peak value and location in the matrix, the decay rate from peak to bottom, and some algebraic constraints of the ...
Wei Kang, Liang Xu
doaj +1 more source
Group Lasso estimation of high-dimensional covariance matrices [PDF]
, 2010 In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a ...
Alvarez, Lilian Muniz +3 more
core +2 more sources
Sample Space-time Covariance Matrix Estimation [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations.
Delaosa, Connor +4 more
openaire +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach
IEEE Access, 2022 This paper is concerned with optimizing the weights of the global minimum-variance portfolio (GMVP) in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing the GMVP weights is highly influenced by
Maaz Mahadi +3 more
doaj +1 more source
On the regularity of the covariance matrix of a discretized scalar field on the sphere
, 2017 We present a comprehensive study of the regularity of the covariance matrix of a discretized field on the sphere. In a particular situation, the rank of the matrix depends on the number of pixels, the number of spherical harmonics, the symmetries of the ...
Barreiro, R. B. +4 more
core +1 more source
CNN-based Realized Covariance Matrix Forecasting [PDF]
, 2021 Yanwen Fang +2 more
openalex +1 more source