New-Type Hoeffding's Inequalities and Application in Tail Bounds
This work provides a more accurate method for evaluating tail bounds, which is beneficial for researchers and practitioners in signal and information processing fields who rely on Hoeffding's inequality.
This paper introduces a new type of Hoeffding's inequalities that incorporates higher-order moments of random variables, unlike previous versions that only considered the first-order moment. This new approach leads to considerable improvements in tail bound evaluations compared to existing results.
It is well known that Hoeffding's inequality has a lot of applications in the signal and information processing fields. How to improve Hoeffding's inequality and find the refinements of its applications have always attracted much attentions. An improvement of Hoeffding inequality was recently given by Hertz \cite{r1}. Eventhough such an improvement is not so big, it still can be used to update many known results with original Hoeffding's inequality, especially for Hoeffding-Azuma inequality for martingales. However, the results in original Hoeffding's inequality and its refinement one by Hertz only considered the first order moment of random variables. In this paper, we present a new type of Hoeffding's inequalities, where the high order moments of random variables are taken into account. It can get some considerable improvements in the tail bounds evaluation compared with the known results. It is expected that the developed new type Hoeffding's inequalities could get more interesting applications in some related fields that use Hoeffding's results.