6
$\begingroup$

Suppose $X$ and $Y$ are two arbitrary random variables, and we have the following inequality that conditional on $Y=y$, $$\textbf{Pr}(X \ge a_0 | Y=y)\le f(y),$$ where $\textbf{Pr}(\cdot)$ denotes the probability of the event, $a_0$ is a constant, and $f(y)$ is an increasing function with respect to $y$. I want to know whether the following inequality is correct, $$\textbf{Pr}(X \ge a_0 , Y\le b_0)\le f(b_0),$$ where $b_0$ is a constant.

If it is wrong, is there any counter example? Thanks a lot.

$\endgroup$
0

2 Answers 2

4
$\begingroup$

Expand using product rule:

$$P(X \ge a_0 \cap Y \le b_0) = P(X \ge a_0 | Y \le b_0) P(Y \le b_0)$$

Assuming $P(Y \le b_0)$ is nonzero, you can use the first inequality to obtain:

$$P(X \ge a_0 | Y \le b_0) P(Y \le b_0) \le f(y\le b_0) P(Y \le b_0)$$

Because $f(y)$ is increasing in $y$, $f(y \le b_0) \le f(b_0)$ so the inequality of interest holds.

By the way, in the context of probability $f$ is normally used for PDFs so you may want to use a different letter to avoid confusion.

EDIT: Granted, $f(y \le b_0)$ is an abuse of notation. It should be read as $f(y), y \le b_0$ and not as the function taking a set for its argument.

$\endgroup$
5
  • 1
    $\begingroup$ This looks like a fine answer, regardless of whether $P(y\le b_0)$ is nonzero. $\endgroup$
    – whuber
    Commented Apr 19, 2017 at 13:53
  • 1
    $\begingroup$ Thank you very much for your answer. The first inequality in the question should be $\textbf{Pr}(X \ge a_0 | Y=y)\le f(y)$, which is more accurate. How can we obtain $\textbf{Pr}(X \ge a_0 | Y \le b_0) \le f(Y \le b_0)$? What does $f(Y \le b_0)$ mean? $\endgroup$
    – Harry
    Commented Apr 19, 2017 at 13:59
  • $\begingroup$ I always saw books made that exception when giving the formula for conditional probability. I never was too sure if it was just to keep zero out of the denominator or if allowing that possibility could have more dire implications that were merely glossed over. Given your comment, I'm assuming it was the former all along! $\endgroup$ Commented Apr 19, 2017 at 14:04
  • 1
    $\begingroup$ @ZHANGWei To be more precise, we should write $P(X \ge a_0 | Y = y) \le f(y), y \le b_0$. This way we avoid making it look like the value of $f$ is random. $\endgroup$ Commented Apr 19, 2017 at 14:12
  • $\begingroup$ @Manuel Fazio I am little confused about the correctness of $P(X \ge a_0 | Y \le b_0) \le f(y\le b_0)$. How can we arrive at this from the first inequality in the question? $\endgroup$
    – Harry
    Commented Apr 19, 2017 at 14:25
3
$\begingroup$

First, we have $$\eqalign{ \textbf{Pr}(X \ge a_0|Y = y) &=\int_{a_0}^{+\infty}p_{X}(x|Y= y)dx \\ &=\int_{a_0}^{+\infty}\frac{p_{X,Y}(x,y)}{p_Y(y)}dx\\ &=\frac{1}{p_Y(y)}\int_{a_0}^{+\infty}{p_{X,Y}(x,y)}dx, }$$ where $p_{X,Y}(x, y)$ is the joint PDF of $(X,Y)$, $p_{X}(x| Y=y)$ is the PDF of $X$ conditional on $Y=y$, and $p_Y(y)$ is the PDF of $Y$. Because when $y \le b_0$, $f(y)\le f(b_0)$, that is $$\int_{a_0}^{+\infty}{p_{X,Y}(x,y)}dx \le f(b_0)p_Y(y) $$ Finally, substitute it into the following, $$\eqalign{ \textbf{Pr}(X \ge a_0, Y \le b_0) &= \int_{a_0}^{+\infty}\int_{-\infty}^{b_0}p_{X,Y}(x, y)dy dx \\ &\le \int_{-\infty}^{b_0} f(b_0)p_Y(y) dy \\ & \le f(b_0), }$$ which proves the required inequality.

$\endgroup$
3
  • $\begingroup$ The notation is bad. Pr(Y=y) should be a probability density for Y. $\endgroup$ Commented Apr 19, 2017 at 18:08
  • $\begingroup$ @Michael Chernick Thanks for your comment, and I have modified the answer. $\endgroup$
    – Harry
    Commented Apr 20, 2017 at 1:27
  • $\begingroup$ That is better. $\endgroup$ Commented Apr 20, 2017 at 1:28

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.