# Proof that "symmetrized" sequence of random variables is independent.

While studying the following theorem from Loeve's book on probability:

Let $X_n$ be uniformly bounded random variables. If $\sum X_n$ converges a.s. then $\sum \sigma^{2}X_n$ and $\sum E X_n$ converges

He writes the proof as follows:

To the random variables $X_n$ we associate random variables $X_n'$ such that $x_n$ and $X_n'$ are identically distributed for every n and $X_1,X_1',X_2,X_2'...$ is a sequence of independent random variables. We form the "symmetrized" sequence $X_{n}^{s} = X_n - X_n'$ of independent random variables.

So I checked the section on symmetrization but I cannot figure out the following:

-How can such a $X_n'$ be built in general? (they need to be independent and identically distributed) -How can I proof that the "symmetrized" sequence is made up with independent random variables?

• apparently it has to do with kolmogorov-daniell theorem Jan 14, 2016 at 11:25
• This is always doable, possibly enlarging the sample space. If the random variables $X_n$ are independent then the random variables $X_n^s$ are independent as well.
– Did
Jan 14, 2016 at 11:37
• how do you know the symmetrised random variables are independent?
– BCLC
Jan 17, 2016 at 18:56
• It is implicit in the quote i made from Loève: " the symmetrized sequence of independent random variables" so that's why I ask how can i build them Jan 17, 2016 at 19:00

Not a mathematician so please let me know if this is completely off base, and I will delete this answer. The way I understand symmetrization, as used in the proof, is the subtraction, from each $X_n$, of an independent RV $X_n'$ with the same distribution as $X_n$. Assuming $X_n$ were independent (not sure if this was supposed to be the case, but is suggested by the proof wherein the sequence $X_n$,$X_n'$ is independent), $X_n'$ will also be independent because they are identically distributed. By the same reason, for all $n$ and $m$, $X_n'$ will be independent of $X_m$, because otherwise $X_n$ would not be independent of $X_m$. Independence of $X_n^s$ follows from 3 independent RVs A,B,C satisfying
$$P_{A-B|C}(a-b<x) = \int_{-\infty}^\infty dP_{B|C}(b) P_{A|C}(a<x+b) = \int_{-\infty}^\infty dP_{B}(b) P_{A}(a<x+b) = P_{A-B}(a-b<x)$$
To use this symmetrized series to show $\sum\sigma^2X_n$ and $\sum EX_n$ converge, note convergence of $\sum\sigma^2X_n$ implies convergence of the latter, and $\sum\sigma^2X_n=1/2\sum\sigma^2X_n^s$, so it suffices to show convergence of $\sum\sigma^2X_n^s$. I will omit this complicated proof, as it seems beyond the scope of the question.
• Indeed this is way offbase. There is no way to prove that $(X'_n)$ and $(X_n)$ are independent, actually one must assume it. I fail to understand your "By the same reason, $X_n'$ will be independent of $X_n$, because otherwise $X_n$ would not be independent of itself." (Maybe the upvoter can explain?)
• @Did sorry for the lack of clarity. I did start with the assumption $X'_n$ and $X_n$ were independent for all $n$, and what I meant in the later statement was for $X'_n$ to be independent of $X_m$ for all n and m. Is that reasonable? I will edit. Aug 10, 2016 at 10:45