{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:58:17Z","timestamp":1791395897691,"version":"4.3.3"},"reference-count":39,"publisher":"Institute of Mathematical Statistics","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ann. Statist."],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:p>We present an estimator of the covariance matrix \u03a3 of random d-dimensional vector from an i.i.d. sample of size n. Our sole assumption is that this vector satisfies a bounded Lp\u2212L2 moment assumption over its one-dimensional marginals, for some p\u22654. Given this, we show that \u03a3 can be estimated from the sample with the same high-probability error rates that the sample covariance matrix achieves in the case of Gaussian data. This holds even though we allow for very general distributions that may not have moments of order &gt; p. Moreover, our estimator can be made to be optimally robust to adversarial contamination. This result improves the recent contributions by Mendelson and Zhivotovskiy and Catoni and Giulini, and matches parallel work by Abdalla and Zhivotovskiy (the exact relationship with this last work is described in the paper).<\/jats:p>","DOI":"10.1214\/24-aos2407","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T01:25:56Z","timestamp":1732152356000},"page":"1953-1977","source":"Crossref","is-referenced-by-count":6,"title":["Improved covariance estimation: Optimal robustness and sub-Gaussian guarantees under heavy tails"],"prefix":"10.1214","volume":"52","author":[{"given":"Roberto I.","family":"Oliveira","sequence":"first","affiliation":[{"name":"Instituto de Matem\u00e1tica Pura e Aplicada (IMPA)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoraida F.","family":"Rico","sequence":"additional","affiliation":[{"name":"Department of Statistics, Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"18","doi-asserted-by":"publisher","unstructured":"<small>Ledoux, M.<\/small> (2001). <i>The Concentration of Measure Phenomenon<\/i>. <i>Mathematical Surveys and Monographs<\/i> <b>89<\/b>. Amer. Math. Soc., Providence, RI.","DOI":"10.1090\/surv\/089"},{"key":"9","doi-asserted-by":"publisher","unstructured":"<small>DEVROYE, L.<\/small>, <small>LERASLE, M.<\/small>, <small>LUGOSI, G.<\/small> and <small>OLIVEIRA, R. I.<\/small> (2016). Sub-Gaussian mean estimators. <i>Ann. Statist.<\/i> <b>44<\/b> 2695\u20132725.","DOI":"10.1214\/16-AOS1440"},{"key":"10","doi-asserted-by":"publisher","unstructured":"<small>DIAKONIKOLAS, I.<\/small>, <small>KAMATH, G.<\/small>, <small>KANE, D.<\/small>, <small>LI, J.<\/small>, <small>MOITRA, A.<\/small> and <small>STEWART, A.<\/small> (2019). Robust estimators in high-dimensions without the computational intractability. <i>SIAM J. Comput.<\/i> <b>48<\/b> 742\u2013864.","DOI":"10.1137\/17M1126680"},{"key":"13","doi-asserted-by":"publisher","unstructured":"<small>HOPKINS, S. B.<\/small> (2020). Mean estimation with sub-Gaussian rates in polynomial time. <i>Ann. Statist.<\/i> <b>48<\/b> 1193\u20131213.","DOI":"10.1214\/19-AOS1843"},{"key":"21","doi-asserted-by":"publisher","unstructured":"<small>LUGOSI, G.<\/small> and <small>MENDELSON, S.<\/small> (2019). Sub-Gaussian estimators of the mean of a random vector. <i>Ann. Statist.<\/i> <b>47<\/b> 783\u2013794.","DOI":"10.1214\/17-AOS1639"},{"key":"20","doi-asserted-by":"publisher","unstructured":"<small>LUGOSI, G.<\/small> and <small>MENDELSON, S.<\/small> (2019). Near-optimal mean estimators with respect to general norms. <i>Probab. Theory Related Fields<\/i> <b>175<\/b> 957\u2013973.","DOI":"10.1007\/s00440-019-00906-4"},{"key":"22","doi-asserted-by":"publisher","unstructured":"<small>LUGOSI, G.<\/small> and <small>MENDELSON, S.<\/small> (2021). Robust multivariate mean estimation: The optimality of trimmed mean. <i>Ann. Statist.<\/i> <b>49<\/b> 393\u2013410.","DOI":"10.1214\/20-AOS1961"},{"key":"31","doi-asserted-by":"publisher","unstructured":"<small>OLIVEIRA, R. I.<\/small> (2016). The lower tail of random quadratic forms with applications to ordinary least squares. <i>Probab. Theory Related Fields<\/i> <b>166<\/b> 1175\u20131194.","DOI":"10.1007\/s00440-016-0738-9"},{"key":"8","doi-asserted-by":"publisher","unstructured":"<small>DEPERSIN, J.<\/small> and <small>LECU\u00c9, G.<\/small> (2022). Robust sub-Gaussian estimation of a mean vector in nearly linear time. <i>Ann. Statist.<\/i> <b>50<\/b> 511\u2013536.","DOI":"10.1214\/21-aos2118"},{"key":"4","doi-asserted-by":"publisher","unstructured":"<small>Catoni, O.<\/small> (2012). Challenging the empirical mean and empirical variance: A deviation study. <i>Ann. Inst. Henri Poincar\u00e9 Probab. Stat.<\/i> <b>48<\/b> 1148\u20131185.","DOI":"10.1214\/11-AIHP454"},{"key":"17","doi-asserted-by":"publisher","unstructured":"<small>Koltchinskii, V.<\/small> and <small>Lounici, K.<\/small> (2017). Concentration inequalities and moment bounds for sample covariance operators. <i>Bernoulli<\/i> <b>23<\/b> 110\u2013133.","DOI":"10.3150\/15-BEJ730"},{"key":"29","doi-asserted-by":"publisher","unstructured":"<small>Minsker, S.<\/small> (2018). Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries. <i>Ann. Statist.<\/i> <b>46<\/b> 2871\u20132903.","DOI":"10.1214\/17-AOS1642"},{"key":"24","doi-asserted-by":"publisher","unstructured":"<small>Mendelson, S.<\/small> and <small>Paouris, G.<\/small> (2014). On the singular values of random matrices. <i>J. Eur. Math. Soc<\/i>. (<i>JEMS<\/i>) <b>16<\/b> 823\u2013834.","DOI":"10.4171\/JEMS\/448"},{"key":"19","doi-asserted-by":"publisher","unstructured":"<small>Lounici, K.<\/small> (2014). High-dimensional covariance matrix estimation with missing observations. <i>Bernoulli<\/i> <b>20<\/b> 1029\u20131058.","DOI":"10.3150\/12-BEJ487"},{"key":"38","doi-asserted-by":"crossref","unstructured":"<small>Vershynin, R.<\/small> (2012). Introduction to the non-asymptotic analysis of random matrices. In <i>Compressed Sensing<\/i> 210\u2013268. Cambridge Univ. Press, Cambridge.","DOI":"10.1017\/CBO9780511794308.006"},{"key":"27","doi-asserted-by":"publisher","unstructured":"<small>Minsker, S.<\/small> (2015). Geometric median and robust estimation in Banach spaces. <i>Bernoulli<\/i> <b>21<\/b> 2308\u20132335.","DOI":"10.3150\/14-BEJ645"},{"key":"15","doi-asserted-by":"crossref","unstructured":"<small>Huber, P. J.<\/small> (1981). <i>Robust Statistics<\/i>. <i>Wiley Series in Probability and Mathematical Statistics<\/i>. Wiley, New York.","DOI":"10.1002\/0471725250"},{"key":"12","unstructured":"<small>Donoho, D.<\/small> and <small>Huber, P. J.<\/small> (1983). The notion of breakdown point. In <i>A Festschrift for Erich L. Lehmann<\/i>. <i>Wadsworth Statist.\/Probab. Ser.<\/i> 157\u2013184. Wadsworth, Belmont, CA."},{"key":"2","doi-asserted-by":"publisher","unstructured":"<small>Boucheron, S.<\/small>, <small>Lugosi, G.<\/small> and <small>Massart, P.<\/small> (2013). <i>Concentration Inequalities<\/i>: <i>A Nonasymptotic Theory of Independence<\/i>. Oxford Univ. Press, Oxford.","DOI":"10.1093\/acprof:oso\/9780199535255.001.0001"},{"key":"1","doi-asserted-by":"publisher","unstructured":"<small>ABDALLA, P.<\/small> and <small>ZHIVOTOVSKIY, N.<\/small> (2024). Covariance estimation: Optimal dimension-free guarantees for adversarial corruption and heavy tails. <i>J. Eur. Math. Soc.<\/i> <b>(online)<\/b>.","DOI":"10.4171\/JEMS\/1505"},{"key":"3","unstructured":"<small>BRAILOVSKAYA, T.<\/small> and <small>VAN HANDEL, R.<\/small> (2022). Universality and sharp matrix concentration inequalities arXiv preprint. Available at arXiv:<a href=\"https:\/\/arxiv.org\/abs\/2201.05142\">2201.05142<\/a>."},{"key":"5","unstructured":"<small>CATONI, O.<\/small> (2016). PAC-Bayesian bounds for the Gram matrix and least squares regression with a random design. arXiv preprint. Available at arXiv:<a href=\"https:\/\/arxiv.org\/abs\/1603.05229\">1603.05229<\/a>."},{"key":"6","unstructured":"<small>CATONI, O.<\/small> and <small>GIULINI, I.<\/small> (2018). Dimension-free PAC-Bayesian bounds for the estimation of the mean of a random vector. <i>Statist. Theory<\/i>. arXiv."},{"key":"7","doi-asserted-by":"publisher","unstructured":"<small>DEPERSIN, J.<\/small> and <small>LECU\u00c9, G.<\/small> (2022). Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms. <i>Probab. Theory Related Fields<\/i> <b>183<\/b> 997\u20131025.","DOI":"10.1007\/s00440-022-01127-y"},{"key":"11","unstructured":"<small>DIAKONIKOLAS, I.<\/small> and <small>KANE, D. M.<\/small> (2019). Recent advances in algorithmic high-dimensional robust statistics. arXiv preprint. Available at arXiv:<a href=\"https:\/\/arxiv.org\/abs\/1911.05911\">1911.05911<\/a>."},{"key":"14","unstructured":"<small>HOPKINS, S. B.<\/small>, <small>LI, J.<\/small> and <small>ZHANG, F.<\/small> (2020). Robust and heavy-tailed mean estimation made simple, via regret minimization. In NeurIPS."},{"key":"16","doi-asserted-by":"publisher","unstructured":"<small>JOLY, E.<\/small>, <small>LUGOSI, G.<\/small> and <small>OLIVEIRA, R. I.<\/small> (2017). On the estimation of the mean of a random vector. <i>Electron. J. Stat.<\/i> <b>11<\/b> 440\u2013451.","DOI":"10.1214\/17-EJS1228"},{"key":"23","doi-asserted-by":"publisher","unstructured":"<small>MENDELSON, S.<\/small> (2021). Approximating <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\" id=\"eq_3\"><msub><mrow><mi mathvariant=\"italic\">L<\/mi><\/mrow><mrow><mi mathvariant=\"italic\">p<\/mi><\/mrow><\/msub><\/math> unit balls via random sampling. <i>Adv. Math.<\/i> <b>386<\/b> Paper No. 107829, 20.","DOI":"10.1016\/j.aim.2021.107829"},{"key":"25","doi-asserted-by":"publisher","unstructured":"<small>MENDELSON, S.<\/small> and <small>ZHIVOTOVSKIY, N.<\/small> (2020). Robust covariance estimation under <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\" id=\"eq_4\"><msub><mrow><mi mathvariant=\"italic\">L<\/mi><\/mrow><mrow><mn>4<\/mn><\/mrow><\/msub><\/math>-<math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\" id=\"eq_5\"><msub><mrow><mi mathvariant=\"italic\">L<\/mi><\/mrow><mrow><mn>2<\/mn><\/mrow><\/msub><\/math> norm equivalence. <i>Ann. Statist.<\/i> <b>48<\/b> 1648\u20131664.","DOI":"10.1214\/19-AOS1862"},{"key":"26","unstructured":"<small>MINASYAN, A.<\/small> and <small>ZHIVOTOVSKIY, N.<\/small> (2023). Statistically optimal robust mean and covariance estimation for anisotropic Gaussians. arXiv preprint. Available at arXiv:<a href=\"https:\/\/arxiv.org\/abs\/2301.09024\">2301.09024<\/a>."},{"key":"28","doi-asserted-by":"publisher","unstructured":"<small>MINSKER, S.<\/small> (2017). On some extensions of Bernstein\u2019s inequality for self-adjoint operators. <i>Statist. Probab. Lett.<\/i> <b>127<\/b> 111\u2013119.","DOI":"10.1016\/j.spl.2017.03.020"},{"key":"30","doi-asserted-by":"publisher","unstructured":"<small>MINSKER, S.<\/small> and <small>WEI, X.<\/small> (2020). Robust modifications of U-statistics and applications to covariance estimation problems. <i>Bernoulli<\/i> <b>26<\/b> 694\u2013727.","DOI":"10.3150\/19-BEJ1149"},{"key":"32","unstructured":"<small>OLIVEIRA, R. I.<\/small> and <small>RESENDE, L.<\/small> (2023). Trimmed sample means for robust uniform mean estimation and regression. arXiv preprint. Available at arXiv:<a href=\"https:\/\/arxiv.org\/abs\/2302.06710\">2302.06710<\/a>."},{"key":"33","doi-asserted-by":"crossref","unstructured":"<small>OLIVEIRA, R. I.<\/small> and <small>RICO, Z. F.<\/small> (2024). Supplement to \u201cImproved covariance estimation: optimal robustness and sub-Gaussian guarantees under heavy tails.\u201d <a href=\"https:\/\/doi.org\/10.1214\/24-AOS2407SUPP\">https:\/\/doi.org\/10.1214\/24-AOS2407SUPP<\/a>","DOI":"10.1214\/24-AOS2407SUPP"},{"key":"34","unstructured":"<small>OSTROVSKII, D. M.<\/small> and <small>RUDI, A.<\/small> (2019). Affine invariant covariance estimation for heavy-tailed distributions. In <i>Proceedings of the Thirty-Second Conference on Learning Theory<\/i> (A. Beygelzimer and D. Hsu, eds.). <i>Proceedings of Machine Learning Research<\/i> <b>99<\/b> 2531\u20132550. PMLR."},{"key":"35","unstructured":"<small>RICO, Z. F.<\/small> (2022). Optimal statistical estimation: Sub-Gaussian properties, heavy-tailed data, and robustness Ph.D. thesis, Instituto de Matem\u00e1tica Pura e Aplicada (IMPA)."},{"key":"36","doi-asserted-by":"publisher","unstructured":"<small>TIKHOMIROV, K.<\/small> (2018). Sample covariance matrices of heavy-tailed distributions. <i>Int. Math. Res. Not. IMRN<\/i> 20 6254\u20136289.","DOI":"10.1093\/imrn\/rnx067"},{"key":"37","doi-asserted-by":"publisher","unstructured":"<small>TROPP, J. A.<\/small> (2015). An introduction to matrix concentration inequalities. <i>Found. Trends<\/i>\u00ae <i>Mach. Learn.<\/i> <b>8<\/b> 1\u2013230.","DOI":"10.1561\/2200000048"},{"key":"39","doi-asserted-by":"publisher","unstructured":"<small>ZHIVOTOVSKIY, N.<\/small> (2024). Dimension-free bounds for sums of independent matrices and simple tensors via the variational principle. <i>Electron. J. Probab.<\/i> <b>29<\/b> Paper No. 13, 28.","DOI":"10.1214\/23-ejp1021"}],"container-title":["The Annals of Statistics"],"original-title":[],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:27:26Z","timestamp":1791394046000},"score":1,"resource":{"primary":{"URL":"https:\/\/projecteuclid.org\/journals\/annals-of-statistics\/volume-52\/issue-5\/Improved-covariance-estimation--Optimal-robustness-and-sub-Gaussian-guarantees\/10.1214\/24-AOS2407.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,10,1]]}},"URL":"https:\/\/doi.org\/10.1214\/24-aos2407","relation":{},"ISSN":["0090-5364","2168-8966"],"issn-type":[{"value":"0090-5364","type":"print"},{"value":"2168-8966","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]}}}