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The Bennett-Orlicz Norm
1University of Washington, Seattle, USA.
Summary
Researchers introduced a new Bennett-Orlicz norm, offering potentially tighter inequalities for expectations of maxima than the existing Bernstein-Orlicz norm. This work explores connections between these norms and various exponential inequalities.
Area of Science:
- Probability theory
- Mathematical analysis
Background:
- The Bernstein-Orlicz norm, introduced by van de Geer and Lederer (2013), is linked to Bernstein-type inequalities.
- Orlicz norms are fundamental in functional analysis and probability theory for bounding random variables.
Purpose of the Study:
- Introduce a novel Bennett-Orlicz norm.
- Establish connections between the new Bennett-Orlicz norm and Bennett-type inequalities.
- Compare the efficacy of the Bennett-Orlicz norm with the Bernstein-Orlicz norm and other established inequalities.
Main Methods:
- Definition and analysis of a new Orlicz norm, termed the Bennett-Orlicz norm.
- Derivation of inequalities for expectations of maxima using the Bennett-Orlicz norm.
- Comparative analysis with existing Bernstein-Orlicz norm and exponential inequalities (Bernstein, Bennett, Prokhorov).
Main Results:
- The newly introduced Bennett-Orlicz norm provides inequalities for expectations of maxima.
- These inequalities are potentially tighter than those derived from the Bernstein-Orlicz norm in applicable scenarios.
- Cross-connections between the Bennett-Orlicz norm, Bernstein-Orlicz norm, and other exponential inequalities are discussed.
Conclusions:
- The Bennett-Orlicz norm offers a valuable new tool in probability theory.
- This norm provides potentially improved bounds for expectations of maxima.
- The study contributes to the understanding of Orlicz norms and their applications in bounding random variables and comparing with existing theoretical frameworks.
Keywords:
Bennett’s inequalityExponential boundMaximal inequalityOrlicz normPoissonPrimary: 60E15, 60F10Prokhorov’s inequalitySecondary: 60G50, 33E20More Related Videos
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