Derive probability density function from joint density function. I have the following problem.
Let $(X,Y)$ be a random vector with joint density function $f(x,y)=8xy$ for $0<x<y<1$.
Find $f_X$ and $f_Y$.
My attempt was:
$f_X(x)=\int_{-\infty}^{\infty}f(x,y)dy=\int_x^1 8xydy=4x(1-x^2) $ 
the second equality from left to right because $x<y<1$.
But my question is, for what $x's$ is valid to say $f_X(x)=4x(1-x^2)$? because x can't be any number, it depends on "$y$" but $f_X(x)$ should depend only for x terms, right?
Thanks in advance.
 A: Clearly the support for the marginal of $X$ is: $(0; 1)$
Because when you "remove" the $y$ you have $0<x<1$
There's no dependence on $y$ because you've 'integrated it out'.
A: It doesn't depend on $y$. If you look at the restrictions on your joint density, you see that $x$ takes values between $0$ and $1$. And as a sanity check, you can check that
$$\int_0^1 f_X(x)dx = \int_0^1 4x(1 - x^2) dx = 1.$$
Edit: As mentioned in the comments, there was a mistake in your original computation of the integral.
Second edit: As for a formal reason why it does not depend on $y$, it's because of your integration: you've marginalized over $x$ and $y$ does not appear anymore.
For an informal reason, think of it this way: your joint density is supported on $0 < x < y < 1$, and so when looking from above, you see that the density has weight only on a triangle (and not on the full square $[0,1]^2$). When you marginalize, you are not looking at the density from above anymore, but from the sides: you cannot see the triangle anymore, you only see that there is some positive weight all along the segment $[0,1]$. 
