The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A well-conditioned estimator for large-dimensional covariance matrices
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The optimal value of α is computed by finding a bias-variance trade-off with no assumption on the data distribution.”
From Signal Estimation and Uncertainties Extraction in Terahertz Time-Domain Spectroscopy (2025) · cites this paper as [55]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Markowitz, H. M. (1952) · The Journal of Finance · doi:10.1111/j.1540-6261.1952.tb01525.x · Cited by 18,429
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
Estimation with Quadratic Loss
James, W., & Stein, C. M. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-0919-5_30 · Cited by 2,319
DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
Marčenko, V. A., & Pastur, L. A. (1967) · Mathematics of the USSR-Sbornik · doi:10.1070/sm1967v001n04abeh001994 · Cited by 1,713
Ledoit, O., & Wolf, M. (2003) · Journal of Empirical Finance · doi:10.1016/s0927-5398(03)00007-0 · Cited by 1,637
Self-citationThe Markowitz Optimization Enigma: Is ‘Optimized’ Optimal?
Michaud, R. O. (1989) · Financial Analysts Journal · doi:10.2469/faj.v45.n1.31 · Cited by 1,384
Strong Convergence of the Empirical Distribution of Eigenvalues of Large Dimensional Random Matrices
Silverstein, J. W. (1995) · Journal of Multivariate Analysis · doi:10.1006/jmva.1995.1083 · Cited by 560
Estimation for Markowitz Efficient Portfolios
Jobson, J. D., & Korkie, B. (1980) · Journal of the American Statistical Association · doi:10.1080/01621459.1980.10477507 · Cited by 555
Limit of the Smallest Eigenvalue of a Large Dimensional Sample Covariance Matrix
Bai, Z., & Yin, Y. (1993) · The Annals of Probability · doi:10.1214/aop/1176989118 · Cited by 546
An Empirical Bayes Approach to Efficient Portfolio Selection
Frost, P. A., & Savarino, J. E. (1986) · Journal of Financial and Quantitative Analysis · doi:10.2307/2331043 · Cited by 391
Research works printed but not linked: 3
- S.A. Aivazyan, I.S. Yenukov, L.D. Meshalkin, Applied Statistics, Reference Edition M., Finances and Statistics, 1985 (Russian).
- Girko, 1986, G-analysis of observations of enormous dimensionality, Calculative Appl. Math., 60, 115
- Girko, 1986, G2-estimations of spectral functions of covariance matrices, Theor. Probab. Math. Statist., 35, 28
Cited by
Sorted by citations
Items 301–400 of 1,003
Risk-aware multi-armed bandit problem with application to portfolio selection
Huo, X., & Fu, F. (2017) · Royal Society Open Science · doi:10.1098/rsos.171377 · Cited by 64
“For δ small, we can improve our estimation by taking advantage of the shrinkage method in Ledoit & Wolf [31].”
MethodREGRESSION ON MANIFOLDS: ESTIMATION OF THE EXTERIOR DERIVATIVE
Aswani, A. J., Bickel, P. J., & Tomlin, C. J. (2010) · Cited by 63
Wang, Q., Li, P., Zuo, W., & Zhang, L. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.480 · Cited by 60
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
Dong, W., & Yao, X. (2008) · Information Sciences · doi:10.1016/j.ins.2008.01.021 · Cited by 60
On Spatial Power Spectrum and Signal Estimation Using the Pisarenko Framework
Stoica, P., Li, J., & Tan, X. G. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.928935 · Cited by 60
Lattice QCD determination of neutron-antineutron matrix elements with physical quark masses
Rinaldi, E., Syritsyn, S. N., Wagman, M. L., Buchoff, M. I., Schroeder, C., & Wasem, J. V. (2019) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.99.074510 · Cited by 60
Principles and open questions in functional brain network reconstruction
Korhonen, O., Zanin, M., & Papo, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25462 · Cited by 59
Theoretical Analysis of Bayesian Matrix Factorization
Nakajima, S., & Sugiyama, M. (2011) · Cited by 59
A Signal Processing Perspective on Financial Engineering
Feng, Y., & Palomar, D. P. (2016) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000072 · Cited by 59
“Asymptotic Analysis (large-dimensional) general asymptotics [121, 122] random matrix theory [199]”
BackgroundMethodSolving norm constrained portfolio optimization via coordinate-wise descent algorithms
Yen, Y.-M., & Yen, T.-J. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.07.010 · Cited by 59
THE JOINT DISTRIBUTION OF STOCK RETURNS IS NOT ELLIPTICAL
Chicheportiche, R., & Bouchaud, J. (2010) · International Journal of Theoretical and Applied Finance · doi:10.1142/s0219024912500197 · Cited by 59
Multi-period portfolio selection with drawdown control
Nystrup, P., Boyd, S., Lindström, E., & Madsen, H. (2018) · Annals of Operations Research · doi:10.1007/s10479-018-2947-3 · Cited by 59
“The squared `2 penalty shrinks together holdings in correlated assets; it corresponds to adding a diagonal matrix to the forecasted covariance matrix in (3), similar to a Stein-type shrinkage estimator (Ledoit and Wolf, 2004).”
High‐dimensional covariance matrix estimation
Lam, C. (2019) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1485 · Cited by 58
“Ledoit and Wolf (2017) applied Σ̂NS to portfolio allocation and also developed further a portfolio allocation strategy using a mean target return. Engle, Ledoit, and Wolf (2019) implemented Σ̂NS into its dynamic conditional correlation framework, such that the updating equation of the dynamic correlation matrix is bas…”
BackgroundMethodInfluentialTsvetanov, K. A., Gazzina, S., Jones, P. S., Swieten, J. C. V., Borroni, B., Sánchez‐Valle, R., Moreno, F., Laforce, R. J., Graff, C., Synofzik, M., et al. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12209 · Cited by 62
Robust estimation of precision matrices under cellwise contamination
Tarr, G., Müller, S., & Weber, N. C. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2015.02.005 · Cited by 58
Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction
Jones, G. S., Stott, P. A., & Mitchell, J. F. B. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd024337 · Cited by 58
Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms
Manolakis, D. G., Lockwood, R. B., & COOLEY, T. W. (2016) · Cited by 58
Estimating Infection Sources in Networks Using Partial Timestamps
Tang, W., Ji, F. Q., & Tay, W. P. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2837655 · Cited by 58
Asset Allocation and Risk Assessment with Gross Exposure Constraints for Vast PortfoliosPreprint
B, J. F. A., Zhang, J., & Yu, K. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1307423 · Cited by 58
Multi-Target Shrinkage Estimation for Covariance Matrices
Lancewicki, T., & Aladjem, M. E. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2364784 · Cited by 57
Rubio, F. R., Mestre, X., & Palomar, D. P. (2012) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2202634 · Cited by 57
Shrinkage estimation of high dimensional covariance matrices
Chen, Y., Wiesel, A., & Hero, A. O. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4960239 · Cited by 57
Durrant, R. J., & Kabán, A. (2014) · Machine Learning · doi:10.1007/s10994-014-5466-8 · Cited by 57
Numerical implementation of the QuEST function
Ledoit, O., & Wolf, M. (2017) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2017.06.004 · Cited by 57
Self-citationRibes, A., Azäis, J., & Planton, S. (2009) · Climate Dynamics · doi:10.1007/s00382-009-0561-4 · Cited by 57
Imaging genetics via sparse canonical correlation analysis
Chi, E. C., Allen, G. I., Zhou, H., Omid Kohannim, A., Lange, K. L., & Thompson, P. M. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556581 · Cited by 56
Posterior convergence rates for estimating large precision matrices using graphical models
Banerjee, S., & Subhashis Ghosal, B. (2014) · Electronic Journal of Statistics · doi:10.1214/14-ejs945 · Cited by 56
A subject-independent brain-computer interface based on smoothed, second-order baselining
Reuderink, B., Farquhar, J. D. R., Poel, M., & Nijholt, A. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091139 · Cited by 55
A Stochastic Model Predictive Control Framework for Stationary Battery Systems
Kumar, R., Wenzel, M. J., Ellis, M. J., ElBsat, M. N., Drees, K. H., & Zavala, V. M. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2789118 · Cited by 56
Reward Boosts Neural Coding of Task Rules to Optimize Cognitive Flexibility
Hall-McMaster, S., Muhle-Karbe, P. S., Myers, N. E., & Stokes, M. G. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0631-19.2019 · Cited by 56
Galaxy and mass assembly: Redshift space distortions from the clipped galaxy field
Simpson, F., Blake, C., Peacock, J. A., Baldry, I. K., Bland‐Hawthorn, J., Heavens, A., Heymans, C., Loveday, J., & Norberg, P. (2016) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.93.023525 · Cited by 56
Brier, M. R., Mitra, A., McCarthy, J. E., Ances, B. M., & Snyder, A. Z. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.039 · Cited by 57
The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks
Guebila, M. B., Wang, T., Lopes‐Ramos, C. M., Fanfani, V., Weighill, D., Burkholz, R., Schlauch, D., Paulson, J. N., Altenbuchinger, M., Shutta, K. H., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02877-1 · Cited by 55
“The magnitude of DRAGON partial correlation values may not be always interpretable without a reference because they are derived from a regularized, shrunken covariance matrix [98].”
BackgroundGuo, S.-M., He, J., Ioannidis, N. M., Sun, G., Wohland, T., & Bathe, M. (2012) · Analytical Chemistry · doi:10.1021/ac2034375 · Cited by 55
A Path Following Algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE)
Rocha, G. V., Zhao, P., & Yu, B. (2008) · Defense Technical Information Center (DTIC) · doi:10.21236/ada487557 · Cited by 60
A Robust Statistics Approach to Minimum Variance Portfolio Optimization
Yang, L., Couillet, R., & McKay, M. R. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2474298 · Cited by 54
Robust adaptive detection of buried pipes using GPR
Hoarau, Q., Ginolhac, G., Atto, A. M., & Nicolas, J. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.07.001 · Cited by 54
Spectrum estimation from samples
Kong, W., & Valiant, G. J. (2017) · The Annals of Statistics · doi:10.1214/16-aos1525 · Cited by 54
Bobadilla-Suarez, S., Ahlheim, C., Mehrotra, A., Panos, A., & Love, B. C. (2019) · Computational Brain & Behavior · doi:10.1007/s42113-019-00068-5 · Cited by 54
“Detecting similarities is critical to a range of cognitive processes and tasks, 2 such as memory retrieval, analogy, decision making, categorization, object 3 recognition, and reasoning [1, 2, 3, 4, 5, 6].”
BackgroundParameter Uncertainty in Multiperiod Portfolio Optimization with Transaction Costs
DeMiguel, V., Martín-Utrera, A., & Nogales, F. J. (2015) · Journal of Financial and Quantitative Analysis · doi:10.1017/s002210901500054x · Cited by 54
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsPreprint
Al-Shedivat, M., Gillenwater, J., Xing, E. P., & Rostamizadeh, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05273 · Cited by 54
“…, denote posterior samples as x j , sample mean as ¯ x (cid:96) := 1 =1 x j , and sample covariance as C.1 The Shrinkage Estimator of the Covariance Ledoit and Wolf [2004b] proposed to estimate a high-dimensional covariance matrix using a convex combination of identity matrix and sample covariance…”
BackgroundKwon, J., Shin, J., & Im, C.-H. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230491 · Cited by 53
“The fNIRS data were segmented into epochs from −1 to 15 s considering that the hemodynamic delay is in the order of several seconds [27].”
MethodEnhanced Portfolio Optimization
Pedersen, L. H., Babu, A., & Levine, A. (2020) · Financial Analysts Journal · doi:10.1080/0015198x.2020.1854543 · Cited by 53
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
Ling, C., Jiang, J., Wang, J., & Zhao, L. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539288 · Cited by 53
Weighted Elastic Net Penalized Mean-Variance Portfolio Design and Computation
Ho, M., Sun, Z., & Xin, J. X. (2015) · SIAM Journal on Financial Mathematics · doi:10.1137/15m1007872 · Cited by 56
Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators
Fritsch, V., Varoquaux, G. P., Thyreau, B., Poline, J., & Thirion, B. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.002 · Cited by 53
On the analysis of cross-correlations in South African market data
Wilcox, D., & Gebbie, T. (2004) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.06.138 · Cited by 53
Unit Root Testing in Heteroscedastic Panels Using the Cauchy Estimator
Demetrescu, M., & Hanck, C. (2012) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2011.638839 · Cited by 53
Principal component analysis for second-order stationary vector time series
Chang, J., Guo, B., & Yao, Q. (2018) · The Annals of Statistics · doi:10.1214/17-aos1613 · Cited by 53
Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
Benaroch, C., Sadatnejad, K., Roc, A., Appriou, A., Monseigne, T., Pramij, S., Mladenović, J., Pillette, L., Jeunet, C., & Lotte, F. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.635653 · Cited by 56
“For describing the EEG epochs as SCMs, we used the optimal linear shrinkage estimator of SCM from Ledoit and Wolf (2004).”
MethodScopes of carbon emissions and their impact on green portfolios
Anquetin, T., Coqueret, G., Tavin, B., & Welgryn, L. (2022) · Economic Modelling · doi:10.1016/j.econmod.2022.105951 · Cited by 47
Li, R., Wu, X., Tian, H., Yu, N., & Wang, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3208465 · Cited by 52
Maximum Likelihood Estimation for Linear Gaussian Covariance Models
Zwiernik, P., Uhler, C., & Richards, D. (2016) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12217 · Cited by 53
Sparse Gaussian graphical models with unknown block structure
Marlin, B. M., & Murphy, K. P. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553465 · Cited by 52
Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces
Kumar, S., Yger, F., & Lotte, F. (2019) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2019.8737349 · Cited by 51
“matrix for motor imagery trials is then estimated using shrinkage based covariance estimator [19] to avoid any numerical problems and provide better estimates, and thus possibly better BCI performances [20].”
On the Realized Risk of High-Dimensional Markowitz Portfolios
Karoui, N. E. (2013) · SIAM Journal on Financial Mathematics · doi:10.1137/090774926 · Cited by 51
Accurate Prostate Volume Estimation Using Multifeature Active Shape Models on T2-weighted MRI
Tóth, R., Bloch, B., Genega, E. M., Rofsky, N. M., Lenkinski, R. E., Rosen, M. A., Kalyanpur, A., Pungavkar, S., & Madabhushi, A. (2011) · Academic Radiology · doi:10.1016/j.acra.2011.01.016 · Cited by 51
Kernel Generalized Canonical Correlation Analysis
Tenenhaus, A., Philippe, C., & Frouin, V. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2015.04.004 · Cited by 49
Method of moments estimation of GO-GARCH models
Boswijk, H. P., & van der Weide, R. (2010) · Journal of Econometrics · doi:10.1016/j.jeconom.2010.11.011 · Cited by 51
Bayesian Information Criterion for Source Enumeration in Large-Scale Adaptive Antenna Array
Huang, L., Xiao, Y.-H., Liu, K., So, H. C., & Zhang, J. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2436060 · Cited by 51
Minimizing the tracking error of cardinality constrained portfolios
Mutunge, P. K., & Haugland, D. (2017) · Computers & Operations Research · doi:10.1016/j.cor.2017.09.002 · Cited by 51
“Based on the historical prices, the covariance matrix Q is then estimated by use of the procedure of Ledoit and Wolf (2004) , which guarantees a non-singular estimate.”
MethodBridging factor and sparse models
Fan, J., Masini, R., & Medeiros, M. C. (2023) · The Annals of Statistics · doi:10.1214/23-aos2304 · Cited by 55
Low-Dimensional Subspace Estimation of Continuous-Doppler-Spread Channel in OTFS Systems
Qu, H., Liu, G., Zhang, L., Imran, M. A., & Wen, S. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3072744 · Cited by 50
“D block-wise transmission of OTFS; 2) matrix H TD is probably ill-conditioned [37]–[39]; 3) it is a sparse matrix.”
BackgroundVisual Learning Induces Changes in Resting-State fMRI Multivariate Pattern of Information
Guidotti, R., Gratta, C. D., Baldassarre, A., Romani, G. L., & Corbetta, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3920-14.2015 · Cited by 49
Shrinkage-to-Tapering Estimation of Large Covariance Matrices
Chen, X., Wang, Z., & McKeown, M. J. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2210546 · Cited by 50
Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study
Yger, F., Lotte, F., & Sugiyama, M. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362879 · Cited by 50
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation
Benidis, K., Sun, Y., Babu, P., & Palomar, D. P. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2605073 · Cited by 55
Stock price prediction using principal components
Ghorbani, M., & Chong, E. K. P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230124 · Cited by 50
“For example Ledoit and Wolf, shrink the sample covariance towards a scaled identity matrix using a shrinkage coefficient that minimizes the mean squared error of the prediction [22].”
MethodToeplitz Structured Covariance Matrix Estimation for Radar Applications
Du, X., Aubry, A., Maio, A. D., & Cui, G. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.2984431 · Cited by 50
Multivariate rotated ARCH models
Noureldin, D., Shephard, N., & Sheppard, K. K. (2013) · Journal of Econometrics · doi:10.1016/j.jeconom.2013.10.003 · Cited by 50
Design-free estimation of variance matrices
Abadir, K. M., Distaso, W., & Žikeš, F. (2014) · Journal of Econometrics · doi:10.1016/j.jeconom.2014.03.010 · Cited by 50
A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and CorrelationsPreprint
Diebold, F. X., & Brandt, M. W. (2005) · SSRN Electronic Journal · doi:10.2139/ssrn.525884 · Cited by 50
Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
Cao, G., & Bouman, C. A. (2008) · Purdue e-Pubs (Purdue University System) · Cited by 49
Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006299 · Cited by 49
Tang, M. F., Ford, L., Arabzadeh, E., Enns, J. T., Visser, T. A. W., & Mattingley, J. B. (2020) · Nature Communications · doi:10.1038/s41467-019-14107-z · Cited by 49
Measures of equity home bias puzzle
Mishra, A. V. (2015) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2015.08.001 · Cited by 49
Habit Formation, Incomplete Markets, and the Significance of Regional Risk for Expected Returns
Korniotis, G. M. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhn074 · Cited by 49
NETS: Network Estimation for Time SeriesPreprint
Barigozzi, M., & Brownlees, C. T. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2249909 · Cited by 52
“The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting.”
Variational study of two-nucleon systems with lattice QCD
Amarasinghe, S. P., Baghdadi, R., Davoudi, Z., Detmold, W., Illa, M., Parreño, A., Pochinsky, A. V., Shanahan, P. E., & Wagman, M. L. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.094508 · Cited by 48
Structured Robust Covariance Estimation
Wiesel, A., & Zhang, T. (2015) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000053 · Cited by 55
Improved information criteria for Bayesian model averaging in lattice field theory
Neil, E. T., & Sitison, J. W. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.014510 · Cited by 63
Bodnar, T., Gupta, A. K., & Parolya, N. (2014) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2014.08.006 · Cited by 48
Super-sparse principal component analyses for high-throughput genomic data
Lee, D., Lee, W., Lee, Y., & Yudi Pawitan, Y. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-296 · Cited by 52
Tsantani, M., Kriegeskorte, N., McGettigan, C., & Garrido, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.017 · Cited by 53
Beyond Risk Parity - A Machine Learning-based Hierarchical Risk Parity Approach on Cryptocurrencies
Burggraf, T., & Vyas, A. (2020) · Finance research letters · doi:10.1016/j.frl.2020.101523 · Cited by 48
Discovering Sparse Covariance Structures With the Isomap
Wagaman, A. S., & Levina, E. (2009) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2009.08021 · Cited by 48
Testing the Assumptions and Predictions of the Hotelling Model
Atewamba, C., & Nkuiya, B. (2015) · Environmental and Resource Economics · doi:10.1007/s10640-015-9922-0 · Cited by 47
A Nodewise Regression Approach to Estimating Large Portfolios
Callot, L., Caner, M., Önder, A. Ö., & Ulaşan, E. (2019) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2019.1683018 · Cited by 47
“Ledoit and Wolf (2004) proposed an estimator for highdimensional covariance matrices that is invertible and well- conditioned.”
BackgroundMethodInfluentialMaximum Likelihood Covariance Estimation with a Condition Number Constraint
Won, J. H., & Kim, S.-J. (2006) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2006.354997 · Cited by 47
Stationary time-vertex signal processing
Loukas, A., & Perraudin, N. (2019) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-019-0631-7 · Cited by 47
“s an independent realization. This approach suces when Nis relatively small. For high-dimensional processes, however, one needs to incorporate additional assumptions to obtain meaningful predictions [2, 3, 4, 5]. In this spirit, this paper focuses on high-dimensional processes that are supported on the vertex set and…”
BackgroundTesting for sphericity in a fixed effects panel data model
Baltagi, B. H., Feng, Q., & Kao, C. D. (2011) · Econometrics Journal · doi:10.1111/j.1368-423x.2010.00331.x · Cited by 45
Robust minimum variance portfolio with L-infinity constraints
Xing, X., Hu, J., & Yang, Y. (2014) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2014.05.004 · Cited by 47
Life-Cycle Finance and the Design of Pension Plans
Bodie, Z., Detemple, J. B., & Rindisbacher, M. (2009) · Annual Review of Financial Economics · doi:10.1146/annurev.financial.050708.144317 · Cited by 45
Vishne, G., Gerber, E. M., Knight, R. T., & Deouell, L. Y. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112752 · Cited by 46
“The regularization parameter l was estimated using the Ledoit-Wolf formula 89 (default implementation in MVPA-Light, 69 for more explanation on the rationale of shrinkage regularization see Blankertz et al. 90 ).”
Collins-Soper kernel from lattice QCD at the physical pion mass
Avkhadiev, A., Shanahan, P. E., Wagman, M. L., & Zhao, Y. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.108.114505 · Cited by 46
Transfer learning promotes acquisition of individual BCI skills
Kumar, S., Alawieh, H., Rácz, F. S., Fakhreddine, R., & Millán, J. D. R. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae076 · Cited by 51
“Its sample wise normalized covariance C , which is used as a feature in the Riemannian geometry framework, is expressed as: We used a shrinkage-based covariance estimator for computing the covariance features C i (Ledoit and Wolf method (56)) in order to avoid any numerical problems.”
MethodSpatio-temporal convolution kernels
Knauf, K., Memmert, D., & Brefeld, U. (2015) · Machine Learning · doi:10.1007/s10994-015-5520-1 · Cited by 46
Classification methods for high-dimensional genetic data
Kalina, J. (2014) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2013.09.007 · Cited by 47
Envelopes and reduced-rank regression
Cook, R. D., Forzani, L., & Zhang, X. (2015) · Biometrika · doi:10.1093/biomet/asv001 · Cited by 46
The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis
Treder, M. S., Porbadnigk, A. K., Avarvand, F. S., Müller, K., & Blankertz, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.019 · Cited by 46
Metadata
- Authors
- Ledoit, O., & Wolf, M.
- Affiliations
- University of California, Los Angeles (US)
- Universitat Pompeu Fabra (ES)
- Corresponding author
- Wolf, Michael
- Venue
- Journal of Multivariate Analysis
- Publisher
- Elsevier BV
- Published
- 2004-02
- Volume
- 88
- Issue
- 2
- Pages
- 365-411
- ISSN
- 0047-259X (ISSN-L)
- OpenAlex
- W2062125287
- Semantic Scholar
- b40890f3d917379ac4bd2e0933a134d45b4dc251
- Cited by
- 3,046
- References
- 24
- Citations by year
- 2026: 355 · 2025: 228 · 2024: 191 · 2023: 178 · 2022: 178 · 2021: 193 · 2020: 198 · 2019: 208 · 2018: 199 · 2017: 146 · 2016: 140 · 2015: 152 · 2014: 137 · 2013: 108 · 2012: 99
- FWCI
- 12.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Multivariate Analysis (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- e-Archivo (Carlos III University of Madrid) (submittedVersion, cc-by-nc-nd)
- Funders
- Yale University
- McDonnell Center for Systems Neuroscience
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Random Matrices and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- covariance matrix estimation, sample covariance matrix, convex combination, asymptotic optimality, squared error loss
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
