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Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey


We survey some of the recent advances in mean estimation and regression function estimation. In particular, we describe sub-Gaussian mean estimators for possibly heavy-tailed data in both the univariate and multivariate settings. We focus on estimators based on median-of-means techniques, but other methods such as the trimmed-mean and Catoni’s estimators are also reviewed. We give detailed proofs for the cornerstone results. We dedicate a section to statistical learning problems—in particular, regression function estimation—in the presence of possibly heavy-tailed data.

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  1. As we explain in what follows, it suffices to ensure that the comparison is correct between \(\mu \) and any point that is not too close to \(\mu \).

  2. In the proof of Theorem 8, “well-behaved” means that (3.5) holds for a majority of the blocks.

  3. The case \(q=3\) is the standard Berry–Esseen theorem, while for \(2<q<3\) one may use generalized Berry–Esseen bounds, see [71].

  4. Note that one has the freedom to select a function \(\widehat{f}\) that does not belong to \({{\mathcal {F}}}\).


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We thank Sam Hopkins, Stanislav Minsker, and Roberto Imbuzeiro Oliveira for illuminating discussions on the subject. We also thank two referees for their thorough reports and insightful comments.

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Correspondence to Gábor Lugosi.

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Communicated by Albert Cohen.

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Gábor Lugosi was supported by the Spanish Ministry of Economy and Competitiveness, Grant MTM2015-67304-P and FEDER, EU, by “High-dimensional problems in structured probabilistic models - Ayudas Fundación BBVA a Equipos de Investigación Cientifica 2017” and by “Google Focused Award Algorithms and Learning for AI.” Shahar Mendelson was supported in part by the Israel Science Foundation.

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Lugosi, G., Mendelson, S. Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey. Found Comput Math 19, 1145–1190 (2019).

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  • Mean estimation
  • Heavy-tailed distributions
  • Robustness
  • Regression function estimation
  • Statistical learning

Mathematics Subject Classification

  • 62G05
  • 62G15
  • 62G35