A New Type Of Upper And Lower Bounds On Right-Tail Probabilities Of Continuous Random Variables
This work provides a novel theoretical tool for probability and statistics, with potential applications in risk assessment and extreme value analysis, though it appears incremental as it builds on existing tail bound methods.
The paper tackles the problem of bounding right-tail probabilities for continuous random variables by introducing new upper and lower bounds that depend on the probability density function, its first derivative, and two parameters, and demonstrates their tightness through numerical examples.
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.