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• W3128339971 abstract "This paper gives a review of concentration inequalities which are widely employed in non-asymptotical analyses of mathematical statistics in a wide range of settings, from distribution-free to distribution-dependent, from sub-Gaussian to sub-exponential, sub-Gamma, and sub-Weibull random variables, and from the mean to the maximum concentration. This review provides results in these settings with some fresh new results. Given the increasing popularity of high-dimensional data and inference, results in the context of high-dimensional linear and Poisson regressions are also provided. We aim to illustrate the concentration inequalities with known constants and to improve existing bounds with sharper constants." @default.
• W3128339971 created "2021-02-15" @default.
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• W3128339971 date "2021-06-01" @default.
• W3128339971 modified "2023-10-13" @default.
• W3128339971 title "Concentration Inequalities for Statistical Inference" @default.
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• W3128339971 doi "https://doi.org/10.4208/cmr.2020-0041" @default.
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