Title
Optimal binomial, Poisson, and normal left-tail domination for sums of nonnegative random variables
Document Type
Article
Publication Date
3-11-2016
Abstract
Exact upper bounds on the generalized moments E ƒ (Sn) of sums Sn of independent nonnegative random variables Xi for certain classes Ƒ of nonincreasing functions ƒ are given in terms of (the sums of) the first two moments of the Xi’s. These bounds are of the form Eƒ(η), where the random variable η is either binomial or Poisson depending on whether n is fixed or not. The classes Ƒ contain, and are much wider than, the class of all decreasing exponential functions. As corollaries of these results, optimal in a certain sense upper bounds on the left-tail probabilities P(Sn ≤ x) are presented, for any real x. In fact, more general settings than the ones described above are considered. Exact upper bounds on the exponential moments Eexp{hSn} for h < 0, as well as the corresponding exponential bounds on the left-tail probabilities, were previously obtained by Pinelis and Utev. It is shown that the new bounds on the tails are substantially better.
Publication Title
Electronic Journal of Probability
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Pinelis, I.
(2016).
Optimal binomial, Poisson, and normal left-tail domination for sums of nonnegative random variables.
Electronic Journal of Probability,
21.
http://doi.org/10.1214/16-EJP4474
Retrieved from: https://digitalcommons.mtu.edu/math-fp/1
Version
Publisher's PDF
Publisher's Statement
© 2016 Institute of Mathematical Statistics. Publisher's version of record: http://dx.doi.org/10.1214/16-EJP4474