Results 11 to 20 of about 924,331 (302)
Quadratic Shrinkage for Large Covariance Matrices [PDF]
Bernoulli, 2020 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.
Olivier Ledoit, Michael Wolf
semanticscholar +6 more sources
Covariance matrices and the separability problem [PDF]
Physical Review Letters, 2006 We propose a unifying approach to the separability problem using covariance matrices of locally measurable observables. From a practical point of view, our approach leads to strong entanglement criteria that allow to detect the entanglement of many bound
J. Eisert +6 more
core +6 more sources
Noisy Covariance Matrices and Portfolio Optimization [PDF]
The European Physical Journal B - Condensed Matter, 2001 According to recent findings [1,2], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be ...
Crisanti +8 more
core +5 more sources
Large Dynamic Covariance Matrices [PDF]
Journal of Business & Economic Statistics, 2017 Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ...
R. Engle, Olivier Ledoit, Michael Wolf
semanticscholar +4 more sources
Principal regression for high dimensional covariance matrices. [PDF]
Electron J Stat, 2021 21 pages of main text and references, 3 ...
Zhao Y +3 more
europepmc +5 more sources
Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations
IEEE Access, 2020 Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise ...
Yanmin Zhu +3 more
doaj +1 more source
A finite-difference method for linearization in nonlinear estimation algorithms [PDF]
Modeling, Identification and Control, 1998 Linearizations of nonlinear functions that are based on Jacobian matrices often cannot be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper.
Tor S. Schei
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Shrinkage Estimators for Covariance Matrices [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels, Michael J., Kass, Robert E.
openaire +3 more sources
On the Properties of Estimates of Monotonic Mean Vectors for Multivariate Normal Distributions [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 .Problems concerning estimation of parameters and determination the statistic, when it is known a priori that some of these parameters are subject to certain order restrictions, are of considerable interest.
Abouzar Bazyari
doaj +1 more source
Remote Sensing, 2022 Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds.
Ronghua Yang +3 more
doaj +1 more source