Given marginal pdfs of X and Y, find pdf of Z=X-Y

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Homework Help Overview

The problem involves finding the probability density function of the random variable Z, defined as Z = X - Y, given the marginal probability density functions of X and Y. The context includes considerations of independence and the relationship between the marginals and the joint distribution.

Discussion Character

  • Exploratory, Assumption checking

Approaches and Questions Raised

  • Participants discuss the need for joint distribution information to proceed with the problem, questioning whether X and Y are independent. Suggestions include transforming Y into a new variable U = -Y to facilitate the analysis.

Discussion Status

There is an ongoing exploration of the implications of independence on the problem. Some participants have suggested using the convolution rule for independent distributions, while others are still seeking clarity on how to connect the marginal distributions to the new variable Z.

Contextual Notes

Participants note that without additional information about the joint distribution of X and Y, particularly regarding their independence, the problem cannot be fully resolved.

ArcanaNoir
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Homework Statement



The probability density function of the random variables X and Y are given by:

f_1(x)= \begin{cases} 2 & -\frac{1}{4}\le x\le \frac{1}{4} \\ 0 & \text{elsewhere} \end{cases}
and
f_2(y) \begin{cases} \frac{1}{2} & 0\le y \le 2 \\ 0 & \text{elsewhere} \end{cases}

respectively.

a) Find the probability density function of the random variable Z=X-Y .
b) What is the probability that Z will assume a value greater than zero?

Homework Equations



Not sure yet.

The Attempt at a Solution



There isn't an example like this in my book. I'm not sure how to go from marginals to the new variable thing, which I couldn't solve in an ordinary manner anyway! Sad sad sad. Am I supposed to make the marginals into a regular f(x,y), or is there some direct way to get to the Z?
 
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I assume your book tells you how to compute the distribution of a sum of random variables such as W=X+Y.

One way to look at this is to invent a new random variable U=-Y. (Use Z=X-Y=X+(-Y)=X+U.) What does the distribution of this variable U look like? of X+U?
 
ArcanaNoir said:

Homework Statement



The probability density function of the random variables X and Y are given by:

f_1(x)= \begin{cases} 2 & -\frac{1}{4}\le x\le \frac{1}{4} \\ 0 & \text{elsewhere} \end{cases}
and
f_2(y) \begin{cases} \frac{1}{2} & 0\le y \le 2 \\ 0 & \text{elsewhere} \end{cases}

respectively.

a) Find the probability density function of the random variable Z=X-Y .
b) What is the probability that Z will assume a value greater than zero?


Homework Equations



Not sure yet.

The Attempt at a Solution



There isn't an example like this in my book. I'm not sure how to go from marginals to the new variable thing, which I couldn't solve in an ordinary manner anyway! Sad sad sad. Am I supposed to make the marginals into a regular f(x,y), or is there some direct way to get to the Z?

Unless you are given more information you cannot do the question:you need to know something about the joint distribution of the pair (X,Y). In particular, are X and Y independent? If they *are* independent, just let Y1 = -Y and look at X+Y1. The distribution of Y1 is easy to get, and surely the distribution of X+Y1 must be obtainable from material in your textbook or notes.

RGV
 
Ray Vickson said:
Unless you are given more information you cannot do the question:you need to know something about the joint distribution of the pair (X,Y). In particular, are X and Y independent? If they *are* independent, just let Y1 = -Y and look at X+Y1. The distribution of Y1 is easy to get, and surely the distribution of X+Y1 must be obtainable from material in your textbook or notes.

RGV

What I typed is all I have.
 
So assume they are independent. As both Ray and I noted, your text or notes must have something to say about the sum of two independent random variables.
 
Hmm, it looks like if they are independent then f(x,y)=f_1(x)f_2(y)
From there, it's like any other random variable problem. Thanks for the suggestion. :)
 
Hi Arcana! :smile:

For adding or subtracting independent distributions, we have the convolution rule for distributions.

Suppose X and Y are independent probability distributions with probability density functions fX(x) and fY(y), and cumulative probability function FX(x) and FY(y).

If U=X+Y, then
P(U \le u) <br /> = P(X + Y \le u) <br /> = \int_{-\infty}^{\infty} f_X(x) P(x+Y \le u) \textrm{ d}x<br /> = \int_{-\infty}^{\infty} f_X(x) P(Y \le u - x) \textrm{ d}x<br />
so
P(U \le u) <br /> = \int_{-\infty}^{\infty} f_X(x) F_Y(u-x) \textrm{ d}x<br />

And if you want to know the probability density of U, we have:
f_U(u)= {d \over du}F_U(u) = {d \over du}P(U \le u)
 
great, thanks!
 

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