Probability Question - Joint density function

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cpatel23
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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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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.