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The main contributions of robust statistics to statistical science and a new challenge

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  • Elvezio Ronchetti

    (University of Geneva)

Abstract

In the first part of the paper, we trace the development of robust statistics through its main contributions which have penetrated mainstream statistics. The goal of this paper is neither to provide a full overview of robust statistics, nor to make a complete list of its tools and methods, but to focus on basic concepts that have become standard ideas and tools in modern statistics. In the second part we focus on the particular challenge provided by high-dimensional statistics and discuss how robustness ideas can be used and adapted to this situation.

Suggested Citation

  • Elvezio Ronchetti, 2021. "The main contributions of robust statistics to statistical science and a new challenge," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 127-135, August.
  • Handle: RePEc:spr:metron:v:79:y:2021:i:2:d:10.1007_s40300-020-00185-3
    DOI: 10.1007/s40300-020-00185-3
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Y. She & K. Chen, 2017. "Robust reduced-rank regression," Biometrika, Biometrika Trust, vol. 104(3), pages 633-647.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. Adarsh Prasad & Arun Sai Suggala & Sivaraman Balakrishnan & Pradeep Ravikumar, 2020. "Robust estimation via robust gradient estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 601-627, July.
    5. She, Yiyuan & Owen, Art B., 2011. "Outlier Detection Using Nonconvex Penalized Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 626-639.
    6. Marco Avella-Medina & Elvezio Ronchetti, 2018. "Robust and consistent variable selection in high-dimensional generalized linear models," Biometrika, Biometrika Trust, vol. 105(1), pages 31-44.
    7. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    8. Qiang Sun & Wen-Xin Zhou & Jianqing Fan, 2020. "Adaptive Huber Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 254-265, January.
    9. McCann, Lauren & Welsch, Roy E., 2007. "Robust variable selection using least angle regression and elemental set sampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 249-257, September.
    10. Xueqin Wang & Yunlu Jiang & Mian Huang & Heping Zhang, 2013. "Robust Variable Selection With Exponential Squared Loss," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 632-643, June.
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    Cited by:

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    2. Paweł D. Domański, 2024. "Energy-Aware Multicriteria Control Performance Assessment," Energies, MDPI, vol. 17(5), pages 1-18, March.

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