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Tight Concentration Inequality for Sub-Weibull Random Variables with Generalized Bernstien Orlicz norm

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arxiv 2302.03850 v2 pith:B3ECHRKW submitted 2023-02-08 math.ST math.PRstat.TH

classification math.STmath.PRstat.TH
keywords randomvariablesboundsconcentrationinequalitiesgeneralizedsub-weibullinequality
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Recent development in high-dimensional statistical inference has necessitated concentration inequalities for a broader range of random variables. We focus on sub-Weibull random variables, which extend sub-Gaussian or sub-exponential random variables to allow heavy-tailed distributions. This paper presents concentration inequalities for independent sub-Weibull random variables with finite Generalized Bernstein-Orlicz norms, providing generalized Bernstein's inequalities and Rosenthal-type moment bounds. The tightness of the proposed bounds is shown through lower bounds of the concentration inequalities obtained via the Paley-Zygmund inequality. The results are applied to a graphical model inference problem, improving previous sample complexity bounds.

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Cited by 1 Pith paper

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  1. Norming Sets for Tensor and Polynomial Sketching

    math.NA 2025-06 conditional novelty 7.0 of 10

    Norming sets are used to bound sketching dimensions for algebraic varieties and polynomial images under arbitrary sketch operators, including a new median sketch that needs only about dim(V) structured measurements.

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