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My prof claims that raising a symmetric r.v., like N(0,1), to an odd power gives a distribution with expectation 0. What's the best way to see this?

Glen_b
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    Symmetry and the law of the unconscious statistician. But a word of caution: "...like a N(0,1) random variable..." is sloppy, imprecise wording on the part of your professor. Such a statement is not, in general, true for any old symmetric distribution. The requisite moments must exist (be well-defined) in the first place. – cardinal Oct 15 '13 at 19:39
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    This assertion is false unless a particularly narrow interpretation of "symmetric" is adopted (and even then @Cardinal's admonition must be heeded): see http://stats.stackexchange.com/a/29010 for more about this. For *familiar* counterexamples in Cardinal's spirit you may contemplate what happens with any Student $t$ distribution with $\nu$ degrees of freedom and you use any odd power greater than or equal to $\nu$. – whuber Oct 15 '13 at 20:55
  • This question appears to be a duplicate of http://stats.stackexchange.com/questions/28992/what-is-the-definition-of-a-symmetric-distribution/29010#29010. – whuber Jan 15 '16 at 15:52

1 Answers1

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The assertion that a symmetric random variable raised to an odd power has mean 0 is not true in general.

(However, it is true that a standard normal raised to an odd power has mean 0.)

To make a general statement, first we'll need to restrict the original variable to have mean zero. That is, our statement would start "a symmetric random variable with mean 0, raised to an odd power..."

Then, we also need the expectation of that odd power to be finite; this is not the case in general. Consider for example the 5th power of a t-distributed random variable (with $\nu=3$, say$).

[Edit: I've only just noticed that whuber offers the same distribution as a counter example (but as a class of distributions, not just a single combination of power and df).]

The integral doesn't converge to a finite number. Since we have symmetry, if $\int_{0}^\infty x^p f(x) dx$ is finite (equal to $a$, say), then the mean of the odd power will be 0 by symmetry:

$E(X^p)=\int_{-\infty}^\infty x^p f(x)\, dx\:$ (law of the unconscious statistician)

$\qquad\quad=\int_{-\infty}^0 x^p f(x)\, dx +\int_{0}^\infty x^p f(x)\, dx$

$\qquad\quad=\int_{-\infty}^0 x^p f(-x)\, dx +\int_{0}^\infty x^p f(x)\, dx\:$ (by symmetry of $f$)

Then let $y=-x$, $dy=-dx$ (watch the negatives carefully, you get three come into it)

$\qquad\quad=-\int_{0}^{\infty} x^p f(x) dx +\int_{0}^\infty x^p f(x)\, dx$

$\qquad\quad=-a+a=0$

Glen_b
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    +1. A more elementary, yet elegant and fully general demonstration, perhaps sheds additional light on the issue. Note that when $X$ is a symmetric variable (about $0$) then $X$ and $-X$ must have the same distribution. (This could be taken as a [definition of "symmetric."](http://stats.stackexchange.com/a/29010)) When $n$ is a positive odd number, then (*assuming $X^n$ has an expectation*) it is almost immediate that $$\mathbb{E}(X^n)=\mathbb{E}((-X)^n)=\mathbb{E}(-X^n)=-\mathbb{E}(X^n).$$ Thus the only value $\mathbb{E}(X^n)$ could possibly have is $0$. – whuber Jan 15 '16 at 15:50
  • @whuber yes, it's certainly much more enlightening than my own post, but it doesn't quite address the issue of what is needed for the expectation to be finite, which I think is important to raise. Still it would be easy to add that to what you have there, and then I think you'd have a top-notch answer.. – Glen_b Jan 15 '16 at 16:54
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    Thanks Glen. I had already posted that answer in a closely related thread at http://stats.stackexchange.com/questions/28992/what-is-the-definition-of-a-symmetric-distribution/29010#29010, so no need for more than a comment here. I believe the finiteness of expectation is neither the focus of the original question nor the real point of the solution: it is needed only to describe the circumstances under which the assertion in the question is true. As you know, there are many threads on this site that discuss finiteness of expectation. – whuber Jan 15 '16 at 18:11