A universal right tail upper bound for supercritical Galton-Watson processes with bounded offspring

TL;DR

This paper establishes a universal exponential upper bound for the tail probabilities of the limiting and finite-time scaled supercritical Galton-Watson processes with bounded offspring, valid across all such distributions with fixed mean and bound.

Contribution

It introduces a nonasymptotic, uniform tail bound for supercritical Galton-Watson processes with bounded offspring, applicable to all distributions with given mean and bound.

Findings

Provides a universal exponential tail bound valid for all generations and offspring distributions.

The bound is explicit and does not depend on specific offspring distribution details.

The tail decay is exponential, offering a practical tool for probabilistic estimates.

Abstract

We consider a supercritical Galton-Watson process $Z_n$ whose offspring distribution has mean $m>1$ and is bounded by some $d\in \{2,3,\ldots\}$. As well-known, the associated martingale $W_n=Z_n/m^n$ converges a.s. to some nonnegative random variable $W_\infty$. We provide a universal upper bound for the right tail of $W_\infty$ and $W_n$, which is uniform in $n$ and in all offspring distributions with given $m$ and $d$, namely: \[ P(W_n\ge x)\le c_1 \exp\left\{-c_2 \frac {m-1}m \frac x d\right\}, \quad \forall n\in \mathbb N \cup \{+\infty\}, \forall x\ge 0, \] for some explicit constants $c_1,c_2>0$. For a given offspring distribution, our upper bound decays exponentially as $x\to \infty$, which is actually suboptimal, but our bound is $\textit{universal}$: it provides a single $\textit{effective}$ expression, which is $\textit{nonasymptotic}$ - it does not require $x$ large - and valid simultaneously for all supercritical bounded offspring distributions.