Let $X$ be a random variable, and $f$ a measurable function. Is there any particular relationship between the expression of $f$ and $corr(f(X),X)$?


### BACKGROUND


The background of asking the value of $corr(f(X),X)$ is as following.


From the book on elementary statistics, I learned the conditional expection $E[Y|X]$ is the
best approximation to the random variable $Y$, under the criterion of minimizing the $E[Y-L(X)]^2$.


Denote by $M(X)$ the $E[Y|X]$, and assume $(X,Y)$ subject to the multivariable normal distribution
with parameters(mean, variance and correlation) $u\_1,u\_2,\sigma\_X^2,\sigma\_Y^2,\rho$.


In this case, ` $M(X)=u\_2+\frac{\rho \sigma\_2}{\sigma\_1}(X - u\_1)$. And computation shows
$$ corr(M(X),X) =\frac{ \sigma\_X}{ \sigma\_M} M'(0) $$. So this seems a nice relation.


### QUESTION


So I just want to know, is it possible to extend the above result to general $(X,f(X))$?


Or, if we restrict $f$ to a special class of functions, is there any nice result on $corr(X,f(X)$?
I tried write $f(X)$ as a power series, but came up with nothing interesting.


If there are some results on it, where can I find the references?


Thank you.



Is there any result discribing the value of the correlation of a measurable function of `$X$` and itself:  `$corr(f(X),X)$` ? 
At least there is a simple result for the case $f(x)=e^{tx}$. Under suitable conditions, we have
$$
{\rm corr}(X,e^{tX} ) = \frac{{{\rm E}[Xe^{tX} ] - \mu \_X {\rm E}[e^{tX} ]}}{{\sigma \_X \sqrt {{\rm E}[e^{2tX} ] - {\rm E}^2 [e^{tX} ]} }} = \frac{{m'\_X (t) - \mu \_X m\_X (t)}}{{\sigma \_X \sqrt {m\_X (2t) - m\_X^2 (t)} }},
$$
where $m\_X (\cdot)$, $\mu\_X$, and $\sigma^2\_X$ denote the moment-generating function, the expectation, and the variance of $X$, respectively.


For example, for $X$ exponential with mean $1/\lambda$ (hence $m\_X (\tau) = \frac{\lambda }{{\lambda - \tau}}$, $\tau \lt \lambda$, $\sigma\_X = 1/\lambda$), this yields
$$
{\rm corr}(X,e^{tX} ) = \frac{{\lambda t}}{{(\lambda - t)^2 \sqrt {\frac{\lambda }{{\lambda - 2t}} - (\frac{\lambda }{{\lambda - t}})^2 } }}
, \;\; t \in ( - \infty ,\lambda /2)\backslash \lbrace 0 \rbrace .
$$
(Using ${\rm E}[X^n] = n!/\lambda^n$, we can also find that ${\rm corr}(X,X^n) = \frac{n}{{\sqrt {{2n \choose n} - 1} }}$.) Note that $\lim \_{t \to 0 \pm } {\rm corr}(X,e^{tX} ) = \pm 1$ and $\lim \_{t \uparrow \lambda /2} {\rm corr}(X,e^{tX} ) = 0$. (For the former, consider $e^{tX} \approx 1 + tX$.)


The general formula for ${\rm corr}(X,e^{tX} )$ may help you reach some conclusion. In particular, note that the functions $f(x)=e^{tx}$ and $m\_X (t)$ are often closely related, see [here](http://en.wikipedia.org/wiki/Moment-generating_function#Examples), for example. 


