A New Type Of Upper And Lower Bounds On Right-Tail Probabilities Of Continuous Random Variables
Probability
2023-11-28 v3 Information Theory
Machine Learning
math.IT
Statistics Theory
Machine Learning
Statistics Theory
Abstract
In this paper, I present a completely new type of upper and lower bounds on the right-tail probabilities of continuous random variables with unbounded support and with semi-bounded support from the left. The presented upper and lower right-tail bounds depend only on the probability density function (PDF), its first derivative, and two parameters that are used for tightening the bounds. These tail bounds hold under certain conditions that depend on the PDF, its first and second derivatives, and the two parameters. The new tail bounds are shown to be tight for a wide range of continuous random variables via numerical examples.
Keywords
Cite
@article{arxiv.2311.12612,
title = {A New Type Of Upper And Lower Bounds On Right-Tail Probabilities Of Continuous Random Variables},
author = {Nikola Zlatanov},
journal= {arXiv preprint arXiv:2311.12612},
year = {2023}
}
Comments
Minor typos corrected v2