Past exam of the mathematics course of the University of Cambridge / 2026 / iii / Paper 210 / 1

For a centered random variable $X$, being sub-Poisson in the right tail with variance parameter ${\sigma }^2$ meansIt is sub-Gamma in the right tail with variance parameter $v$ and scale parameter $c$ when

For $q\ge 2$, $q!\ge 23^{q-2}$. Hence, for $0\le \lambda <3$,Substitution into the defining moment-generating function bound shows that $X$ is sub-Gamma in the right tail with variance parameter ${\sigma }^2$ and scale parameter $1/3$.

Fix $0\le \lambda <c^{-1}$. Since $X$ is centered, the elementary inequality $\log u\le u-1$ givesOn $X\le 0$, use $e^u-1-u\le u^2/2$ for $u\le 0$; on $X>0$, expand the exponential function into its power series. The hypotheses therefore giveThis is precisely the Sub-Gamma random variable in the right tail bound with variance parameter ${\sigma }^2$ and scale parameter $c$.

A centered random variable $Y$ is sub-Gaussian with variance parameter ${\sigma }^2$ whenThe Chernoff bound, applied to $Y$ and $-Y$, yieldsThe tail integral formula for moments and the substitution $u=t^2/(2{\sigma }^2)$ now give

Write $s={\sigma }_Y{\sigma }_Z$ and $W=YZ-\mathbb{E}(YZ)$. The variables $Y$ and $Z$ need not be independent random variables. Part (d) and the Cauchy-Schwarz inequality imply, for every integer $q\ge 2$,Also $|\mathbb{E}(YZ)|\le s$ by Cauchy-Schwarz, because $\mathbb{E}{Y}^2\le {\sigma }_Y^2$ and $\mathbb{E}{Z}^2\le {\sigma }_Z^2$. Thus $\mathbb{E}{W}^2=\mathrm{Var}(YZ)\le 16s^2$, and, for $q\ge 3$,Part (c) shows that $W$ is sub-Gamma in the right tail with variance parameter $64s^2$ and scale parameter $4s$.

If $\mathbb{E}(YZ)\ge 0$, then $W_{+}\le (YZ{)}_{+}\le |YZ|$. Consequentlywhile $\mathbb{E}{W}^2\le 16s^2$. Another application of part (c) gives the improved variance parameter $16{\sigma }_Y^2{\sigma }_Z^2$ and scale parameter $2{\sigma }_Y{\sigma }_Z$.