Probability Question - Joint density function

In summary, the question asks for the joint probability density function (JPDF) of two independent random variables X and Y, defined by Z = X+Y and W = X/(X+Y). To find the JPDF, the formula for the density of a sum of two independent random variables can be used, and the joint density of (X,Y) can be integrated over a specific region in (x,y)-space. The Jacobian is also involved in finding the joint density of (Z,W).
  • #1
cpatel23
16
0
Before I begin, here is the question:

If the PDF of two independent random variables X and Y are:
f(x) = exp(-x)u(x)
f(y) = exp(-y)u(y)
Determine the join probability density function (JPDF) of Z&W defined by:
Z = X+Y
W = X/(X+Y).

So, I know how to solve this except for one thing. How do I get the expression for Z and W.
For Z do I literally just add f(x) + f(y) meaning z = exp(-x) + exp(-y) for (x,y) >0? Same with W?
Once I get the expressions I just find fxy(x,y) and divide by the determinant of the Jacobian. The problem is that the Jacobian depends on the derivative of Z and W which I do not know how to get an expression for.

Please help.
 
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  • #2
The density function for Z is the convolution of the density functions for X and Y. For W it is much more complicated.
 
  • #3
cpatel23 said:
Before I begin, here is the question:

If the PDF of two independent random variables X and Y are:
f(x) = exp(-x)u(x)
f(y) = exp(-y)u(y)
Determine the join probability density function (JPDF) of Z&W defined by:
Z = X+Y
W = X/(X+Y).

So, I know how to solve this except for one thing. How do I get the expression for Z and W.
For Z do I literally just add f(x) + f(y) meaning z = exp(-x) + exp(-y) for (x,y) >0? Same with W?
Once I get the expressions I just find fxy(x,y) and divide by the determinant of the Jacobian. The problem is that the Jacobian depends on the derivative of Z and W which I do not know how to get an expression for.

Please help.

The formula for the density of a sum ##X+Y## of two independent random variables with densities ##f_X(x)## and ##f_Y(y)## is found in every probability textbook, as well as on-line. If you don't know it you can work it out from first principles; one way is to get density ##f_Z(z)## from the fact that
[tex] f_Z(z) \, \Delta z \doteq P(z < X+Y < z + \Delta z) \;\text{as} \: \Delta z \to 0 [/tex]
and to integrate the joint density ##f_X(x) f_Y(y)## over the region ##\{z < x+y < z + \Delta z \}## in ##(x,y)-## space.

To get the joint density of ##(Z,W)##, use the standard transformation formulas that involve Jacobians, etc. The joint density of ##(X,Y)## is ##f_X(x) f_Y(y)##, and certainly does not involve a sum ##f_X(x) + f_Y(y)##, or anything like it.
 

What is a joint density function?

A joint density function is a mathematical function that describes the probability of two or more random variables occurring simultaneously. It gives the probability of a combination of values for the variables, rather than just one variable.

How is a joint density function different from a marginal density function?

A marginal density function describes the probability of one variable occurring, without considering the other variables. A joint density function takes into account the probabilities of all variables occurring together.

How do you calculate the joint density function?

The joint density function can be calculated by finding the product of the individual density functions for each variable. For continuous variables, this is done by finding the joint probability density function, and for discrete variables, it is done by finding the joint probability mass function.

What is the relationship between the joint density function and the joint cumulative distribution function?

The joint density function describes the probability of a specific combination of values for multiple variables. The joint cumulative distribution function, on the other hand, describes the probability of a specific range of values for those same variables. The two are related by taking the integral of the joint density function over the desired range of values.

How is the joint density function used in statistics?

The joint density function is commonly used in statistics for analyzing the relationships between multiple variables and predicting outcomes. It is also used in probability theory to model and analyze complex systems and processes.

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