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  • Search: subject:"Large-dimensional asymptotics"
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Year of publication
Subject
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Large-dimensional asymptotics 24 rotation equivariance 21 random matrix theory 18 large-dimensional asymptotics 16 Estimation theory 15 Schätztheorie 15 Correlation 12 Korrelation 12 nonlinear shrinkage 11 Linear algebra 9 Lineare Algebra 9 Markowitz portfolio selection 9 Portfolio selection 7 Portfolio-Management 7 nonlinear shrinkage estimation 7 Hilbert transform 6 Monte-Carlo-Simulation 6 Stein's loss 5 factor models 5 Monte Carlo simulation 4 Stein shrinkage 4 Theorie 4 signal amplitude 4 Covariance matrix estimation 3 Inverse shrinkage 3 Random matrix theory 3 Statistical theory 3 Statistische Methodenlehre 3 Varianzanalyse 3 covariance matrix eigenvalues 3 dynamic conditional correlations 3 numerical optimization 3 principal component analysis 3 spectrum estimation 3 Analysis of variance 2 Dynamic conditional correlations 2 Eigenwert 2 Kernel estimation 2 Kovarianzfunktion 2 Modellierung 2
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Online availability
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Free 32 Undetermined 9
Type of publication
All
Book / Working Paper 37 Article 4
Type of publication (narrower categories)
All
Working Paper 33 Arbeitspapier 16 Graue Literatur 16 Non-commercial literature 16 Article in journal 3 Aufsatz in Zeitschrift 3
Language
All
English 36 Undetermined 5
Author
All
Ledoit, Olivier 36 Wolf, Michael 36 Bodnar, Taras 5 Parolya, Nestor 5 Mazur, Stepan 2 Gupta, Arjun K. 1 Ngailo, Edward 1 Okhrin, Yarema 1 Schmid, Wolfgang 1
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Institution
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Institut für Volkswirtschaftslehre, Wirtschaftswissenschaftliche Fakutät 4
Published in...
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Working Paper 17 Working paper series / University of Zurich, Department of Economics 16 ECON - Working Papers 3 European journal of operational research : EJOR 1 IEW - Working Papers 1 Journal of Multivariate Analysis 1 Journal of business & economic statistics : JBES ; a publication of the American Statistical Association 1 Journal of financial econometrics 1
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Source
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ECONIS (ZBW) 19 EconStor 17 RePEc 5
Showing 1 - 10 of 41
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Optimal shrinkage-based portfolio selection in high dimensions
Bodnar, Taras; Okhrin, Yarema; Parolya, Nestor - In: Journal of business & economic statistics : JBES ; a … 41 (2023) 1, pp. 140-156
Persistent link: https://www.econbiz.de/10013540653
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Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Ledoit, Olivier; Wolf, Michael - 2021
matrix estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional … asymptotics. In addition, we compare via Monte Carlo simulations our methodology to two simpler ones from the literature, linear …
Persistent link: https://www.econbiz.de/10012588496
Saved in:
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Shrinkage estimation of large covariance matrices : keep it simple, statistician?
Ledoit, Olivier; Wolf, Michael - 2021 - This version: June 2021
matrix estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional … asymptotics. In addition, we compare via Monte Carlo simulations our methodology to two simpler ones from the literature, linear …
Persistent link: https://www.econbiz.de/10012584105
Saved in:
Cover Image
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Ledoit, Olivier; Wolf, Michael - 2020
matrix estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional … asymptotics. In addition, we compare via Monte Carlo simulations our methodology to two simpler ones from the literature, linear …
Persistent link: https://www.econbiz.de/10012166459
Saved in:
Cover Image
The power of (non-)linear shrinking: A review and guide to covariance matrix estimation
Ledoit, Olivier; Wolf, Michael - 2020
Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance...
Persistent link: https://www.econbiz.de/10012166460
Saved in:
Cover Image
Quadratic shrinkage for large covariance matrices
Ledoit, Olivier; Wolf, Michael - 2020
) estimator in finite samples and recent progress under large-dimensional asymptotics. The estimator keeps the eigenvectors of the … no cubic or higherorder nonlinearities beat quadratic with respect to Frobenius loss under large-dimensional asymptotics …
Persistent link: https://www.econbiz.de/10012420686
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Cover Image
Quadratic shrinkage for large covariance matrices
Ledoit, Olivier; Wolf, Michael - 2020 - Revised version
) estimator in finite samples and recent progress under large-dimensional asymptotics. The estimator keeps the eigenvectors of the … no cubic or higherorder nonlinearities beat quadratic with respect to Frobenius loss under large-dimensional asymptotics …
Persistent link: https://www.econbiz.de/10012390074
Saved in:
Cover Image
The power of (non-)linear shrinking : a review and guide to covariance matrix estimation
Ledoit, Olivier; Wolf, Michael - 2020 - This version: February 2020
Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance...
Persistent link: https://www.econbiz.de/10012165719
Saved in:
Cover Image
The power of (non-)linear shrinking: A review and guide to covariance matrix estimation
Ledoit, Olivier; Wolf, Michael - 2019
Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance...
Persistent link: https://www.econbiz.de/10012026512
Saved in:
Cover Image
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Ledoit, Olivier; Wolf, Michael - 2019
estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional … asymptotics. We solve the problem of optimal covariance matrix estimation under a variety of loss functions motivated by …
Persistent link: https://www.econbiz.de/10012040363
Saved in:
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