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Generalizations of Some Concentration Inequalities

Functional Analysis 2022-09-14 v3 Probability

Abstract

For a real-valued measurable function ff and a nonnegative, nondecreasing function ϕ\phi, we first obtain a Chebyshev type inequality which provides an upper bound for ϕ(λ1)μ({xΩ:f(x)λ1})+k=2n(ϕ(λk)ϕ(λk1))μ({xΩ:f(x)λk}),\displaystyle \phi(\lambda_{1}) \mu(\{x \in \Omega : f(x) \geq \lambda_{1} \}) + \sum_{k=2}^{n}\left(\phi(\lambda_{k})- \phi(\lambda_{k-1})\right) \mu(\{x \in \Omega : f(x) \geq \lambda_{k}\}) , where 0<λ1<λ2λn<0 < \lambda_1 < \lambda_2 \cdots \lambda_n < \infty. Using this, generalizations of a few concentration inequalities such as Markov, reverse Markov, Bienaym\'e-Chebyshev, Cantelli and Hoeffding inequalities are obtained.

Keywords

Cite

@article{arxiv.2108.01479,
  title  = {Generalizations of Some Concentration Inequalities},
  author = {M. Ashraf Bhat and G. Sankara Raju Kosuru},
  journal= {arXiv preprint arXiv:2108.01479},
  year   = {2022}
}

Comments

11 pages

R2 v1 2026-06-24T04:47:26.295Z