Bounds on Tail Probabilities in Exponential families

TL;DR

This paper introduces new inequalities for tail probabilities within key exponential families, utilizing variance functions and signed log-likelihood to establish bounds and distributional approximations.

Contribution

It provides novel tail probability inequalities for important exponential families using variance functions and signed log-likelihood, enhancing probabilistic bounds.

Findings

New inequalities for tail probabilities in exponential families

Bounds expressed via signed log-likelihood and variance functions

Distributional intersection properties close discrete and continuous distributions

Abstract

In this paper we present various new inequalities for tail proabilities for distributions that are elements of the most improtant exponential families. These families include the Poisson distributions, the Gamma distributions, the binomial distributions, the negative binomial distributions and the inverse Gaussian distributions. All these exponential families have simple variance functions and the variance functions play an important role in the exposition. All the inequalities presented in this paper are formulated in terms of the signed log-likelihood. The inequalities are of a qualitative nature in that they can be formulated either in terms of stochastic domination or in terms of an intersection property that states that a certain discrete distribution is very close to a certain continuous distribution.