Finding the probability $ \mathsf{P}(2 X > Y) $ for a certain pair $ (X,Y) $ of independent random variables.

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Just a heads-up: This is for a homework assignment so feel free to point in the right direction rather than answer it.

Suppose that $ X $ and $ Y $ are random variables whose joint p.d.f. $ f $ is defined by $$ \forall (x,y) \in [0,1]^{2}: \qquad {f_{X,Y}}(x,y) \stackrel{\text{df}}{=} 4 x y. $$

I’ve found that $ X $ and $ Y $ are independent (with the marginal p.d.f.’s $ {f_{X}}(x) = 2 x $ and $ {f_{Y}}(y) = 2 y $). Now, I’m asked to find $ \mathsf{P}(2 X > Y) $.

I’m not even sure where to start with this. At this point, I’m thinking (because of an example in our text) that I have to use the identity $$ \mathsf{P}(2 X > Y) = \mathsf{P}(2 X - Y > 0) = 1 - \mathsf{P}(2 X - Y \leq 0) $$ to integrate $ f_{X,Y} $ over the region $ \{ (x,y) \mid 0 \leq x \leq 1 ~ \text{and} ~ 0 \leq y \leq 2 x \} $. However, I’m not sure why or if I’m allowed to use the joint p.d.f. when we’ve already done stuff to $ X $ and $ Y $.

Am I allowed to? Or am I completely on the wrong course (I feel like it’s probably this one, haha)?

Thanks in advance.


Edit: Fixed up the specification of the region. -_-

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... to integrate $f_{X,Y}$ over the region $\{(x,y)\mid 0 \le y \le 2x,~0 \le x \le 1\}$, but I'm not sure why or if I'm allowed to use the joint distribution when we've done stuff to $X$ and $Y$. Am I allowed to? Or am I on completely the wrong course (I feel like it's probably this one haha)?

Yes, almost.   You want to integrate over the region within the joint support of the random variables where the constraint is holds.

That is: $\{(x,y)\mid 0\leq x\le 1, 0\le y\le 1, 2x>y\}\\ = \{(x,y)\mid 0\leq x\le 1/2, 0\le y< 2x\}\cup\{(x,y)\mid 1/2< x\le 1, 0\le y\le 1\}$

$$\begin{align}\mathsf P(2X>Y) ~=~& \iint_{2x>y} f_{X,Y}(x,y)~\mathsf d(x, y)\\[1.25ex]=~& \int_0^1\int_0^{\min(1, 2x)} 4xy~\mathsf d y~\mathsf d x\\[1ex]=~& \int_0^{1/2}\int_0^{2x}4xy~\mathsf d y~\mathsf d x+\int_{1/2}^1\int_0^1 4xy~\mathsf d y~\mathsf d x\end{align}$$