Month: January 2019

This post is devoted to a quick proof of a version of the law of large numbers for non-negative random variables in $\mathrm{L}^2$. It does not require independence or same distribution. It is a variant of the famous one for independent random variables bounded in $\mathrm{L}^4$.

The statement. If $X_1,X_2,\ldots$ are non-negative $\mathrm{L}^2$ random variables on $(\Omega,\mathcal{A},\mathbb{P})$ such that $\lim_{n\to\infty}\frac{1}{n}\mathbb{E}(S_n)=\ell\in\mathbb{R}$, and $\mathrm{Var}(S_n)=\mathcal{O}(n)$ where $S_n:=X_1+\cdots+X_n$, then $$\lim_{n\to\infty}\frac{S_n}{n}=\ell\text{ almost surely}.$$

A proof. We have $$\mathbb{E}\Bigr(\sum_n\Bigr(\frac{S_{n^2}-\mathbb{E}(S_{n^2})}{n^2}\Bigr)^2\Bigr)=\sum_n\frac{\mathrm{Var}(S_{n^2})}{n^4}=\mathcal{O}\Bigr(\sum_n\frac{1}{n^2}\Bigr)<\infty.$$ It follows that $\sum_n\Bigr(\frac{S_{n^2}-\mathbb{E}(S_{n^2})}{n^2}\Bigr)^2<\infty$ almost surely, which implies that $\frac{S_{n^2}-\mathbb{E}(S_{n^2})}{n^2}\to0$ almost surely. This gives that $\frac{1}{n^2}S_{n^2}\to\ell$ almost surely. It remains to use the sandwich $$\frac{S_{n^2}}{n^2}\frac{n^2}{(n+1)^2}\leq\frac{S_k}{k}\leq\frac{S_{(n+1)^2}}{(n+1)^2}\frac{(n+1)^2}{n^2}$$ valid if $k$ is such that $n^2\leq k\leq (n+1)^2$ (here we use the non-negative nature of the $X$'s).

Note and further reading. I have learnt this proof recently from my friend Arnaud Guyader who found it in a paper (Theorem 5) by Bernard Delyon and François Portier. It is probably a classic however. It is possible to drop the assumption of being non-negative by using $X=X_+-X_-$, but this would require to modify the remaining assumptions, increasing the complexity, see for instance Theorem 6.2 in Chapter 3 of Erhan Çinlar book (and the blog post comments below). The proof above has the advantage of being quick and beautiful.