Probability: What are p.d.f.'s of x+y and x/y?

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Homework Help Overview

The problem involves finding the probability density functions (p.d.f.s) of the sums and ratios of two random variables, X and Y, given their joint distribution. The joint distribution is defined for positive values of X and Y.

Discussion Character

  • Mixed

Approaches and Questions Raised

  • Participants explore transformations to derive the p.d.f.s of X + Y and X/Y, discussing Jacobians and integration techniques.
  • Some participants question the existence of the expectation of X/Y and the conditions under which the joint distribution is defined.
  • Clarifications are sought regarding the definitions of certain functions used in the derivations.

Discussion Status

The discussion has evolved with participants providing corrections and clarifications regarding the joint distribution and the definitions of the functions involved. There is acknowledgment of mistakes in earlier posts, particularly concerning the conditions for the joint distribution and the calculations for expectations.

Contextual Notes

There is a noted issue with the original joint density function not being properly defined for all values of Y, leading to questions about the validity of the calculations. Participants also highlight the importance of clear notation in mathematical expressions.

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Homework Statement



The probability desity function (p.d.f.) of joint distribution of random variables X and Y is given as

f(x,y) = \begin{cases} e^{-(x + y)} \;\; when \;\; x > 0 \\ 0 \;\; \;\;\;\;\;\;\;\;\;\;otherwise \end{cases}

Question 1: What are the p.d.f.'s of X + Y and X/Y ?

Question 2: Does the expectation of X/Y exist ?

Homework Equations



Nothing special.

The Attempt at a Solution



Answer 1:

\begin{cases} u = x \\ v = x + y \end{cases}

\begin{cases} x = u \\ y = v - u \end{cases}

Jacobian = \begin{bmatrix} \frac{dx}{du} & \frac{dx}{dv} \\\frac{dy}{du} & \frac{dy}{dv} \end{bmatrix} = \begin{bmatrix}1 & 0 \\{-1} & 1 \end{bmatrix}

g(u,v)=f(u,v-u)|Jacobian|= e^{-v}

h(x+y)=h(v) = \int_0^v g(u,v)du = e^{-v} u |_{u=0}^{u=v} = ve^{-v}

\begin{cases}z = x\\ w = \frac{x}{y} \end{cases}

\begin{cases} x = z\\ y = \frac{z}{w} \end{cases}

Jacobian2 = \begin{bmatrix} \frac{dx}{dz} & \frac{dx}{dw} \\\frac{dy}{dz} & \frac{dy}{dw} \end{bmatrix} = \begin{bmatrix}1 & 0 \\{ \frac{1}{w} } & {- \frac{z}{ w^{2}} } \end{bmatrix}

g2(z,w)=f(z, \frac{z}{w} )|Jacobian2|= e^{-z- \frac{z}{w} } \frac{z}{ w^{2} }

h2(w)= \int_0^ \infty g2(z,w)dz = \int_0^ \infty e^{-z- \frac{z}{w} } \frac{z}{ w^{2} } dz = -\int_0^ \infty \frac{z}{ w^{2} } {(1+ \frac{1}{w} )}^{-1} d e^{-z(1+ \frac{1}{w})} = 0 + \int_0^\infty \frac{e^{-z(1+ \frac{1}{w})}}{ w^{2} \frac{1}{w}} dz = - \frac{1}{ {w+1}^{2} } e^{-z(1+ \frac{1}{w})} |_{z=0}^{z= \infty }= \frac{1}{{w+1}^{2}}

Answer 2:

E( \frac{x}{y} )=E(w)= \int_0^\infty \frac{1}{{w+1}^{2}} dw = -\frac{1}{w+1}|_{w=0}^{w=\infty} = 1

Are the two answers correct? Thank you in advance.
 
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sanctifier said:

Homework Statement



The probability desity function (p.d.f.) of joint distribution of random variables X and Y is given as

f(x,y) = \begin{cases} e^{-(x + y)} \;\; when \;\; x > 0 \\ 0 \;\; \;\;\;\;\;\;\;\;\;\;otherwise \end{cases}

Question 1: What are the p.d.f.'s of X + Y and X/Y ?

Question 2: Does the expectation of X/Y exist ?

Homework Equations



Nothing special.

The Attempt at a Solution



Answer 1:

\begin{cases} u = x \\ v = x + y \end{cases}

\begin{cases} x = u \\ y = v - u \end{cases}

Jacobian = \begin{bmatrix} \frac{dx}{du} & \frac{dx}{dv} \\\frac{dy}{du} & \frac{dy}{dv} \end{bmatrix} = \begin{bmatrix}1 & 0 \\{-1} & 1 \end{bmatrix}

g(u,v)=f(u,v-u)|Jacobian|= e^{-v}

h(x+y)=h(v) = \int_0^v g(u,v)du = e^{-v} u |_{u=0}^{u=v} = ve^{-v}

\begin{cases}z = x\\ w = \frac{x}{y} \end{cases}

\begin{cases} x = z\\ y = \frac{z}{w} \end{cases}

Jacobian2 = \begin{bmatrix} \frac{dx}{dz} & \frac{dx}{dw} \\\frac{dy}{dz} & \frac{dy}{dw} \end{bmatrix} = \begin{bmatrix}1 & 0 \\{ \frac{1}{w} } & {- \frac{z}{ w^{2}} } \end{bmatrix}

g2(z,w)=f(z, \frac{z}{w} )|Jacobian2|= e^{-z- \frac{z}{w} } \frac{z}{ w^{2} }

h2(w)= \int_0^ \infty g2(z,w)dz = \int_0^ \infty e^{-z- \frac{z}{w} } \frac{z}{ w^{2} } dz = -\int_0^ \infty \frac{z}{ w^{2} } {(1+ \frac{1}{w} )}^{-1} d e^{-z(1+ \frac{1}{w})} = 0 + \int_0^\infty \frac{e^{-z(1+ \frac{1}{w})}}{ w^{2} \frac{1}{w}} dz = - \frac{1}{ {w+1}^{2} } e^{-z(1+ \frac{1}{w})} |_{z=0}^{z= \infty }= \frac{1}{{w+1}^{2}}

Answer 2:

E( \frac{x}{y} )=E(w)= \int_0^\infty \frac{1}{{w+1}^{2}} dw = -\frac{1}{w+1}|_{w=0}^{w=\infty} = 1

Are the two answers correct? Thank you in advance.

There is something wrong with the question as stated: your joint density ##f(x,y)## blows up as ##y \to -\infty##, and does not have a finite integral over ##y \in (-\infty,\infty)##. You restrict x but not y.

You never define for us what is meant by ##h(w)## and ##h2(w)##, so we end up having to try to guess---a good way to lose marks on an assignment.

Also, when you write
\frac{1}{w+1^2}
you are writing
\frac{1}{w+1}
If you really mean
\frac{1}{(w+1)^2}
then use parentheses. BTW: that last form is the correct density of ##X/Y## at ##w \geq 0##.

Finally, the integral you need for ##E(X/Y)## is not the integral you wrote.
 
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Thank you very much for your reply, Ray.

The p.d.f. should be

f(x,y) = \begin{cases} e^{-(x + y)} \;\; when \;\; x > 0 \;\; y > 0\\ 0 \;\; \;\;\;\;\;\;\;\;\;\;otherwise \end{cases}

I forgot to write the part y > 0, my mistake.

h(v)=h(x+y) actually is the marginal p.d.f. of v

Similarly, h2(w) =h2( \frac{x}{y} ) is the marginal p.d.f. of w

The derivation of h2(w) should be

h2(w)= \int_0^ \infty g2(z,w)dz = \int_0^ \infty e^{-z- \frac{z}{w} } \frac{z}{ w^{2} } dz = -\int_0^ \infty \frac{z}{ w^{2} } {(1+ \frac{1}{w} )}^{-1} d e^{-z(1+ \frac{1}{w})} = 0 + \int_0^\infty \frac{e^{-z(1+ \frac{1}{w})}}{ w^{2} \frac{1}{w}} dz = - \frac{1}{ {(w+1)}^{2} } e^{-z(1+ \frac{1}{w})} |_{z=0}^{z= \infty }= \frac{1}{{(w+1)}^{2}}

Yes, as you said, I lost the parentheses when writing \frac{1}{{(w+1)}^{2}}

The last integral is wrong, it should be

E( \frac{x}{y} )=E(w)=\int_0^ \infty w \frac{1}{{(w+1)}^{2}} dw = \int_0^ \infty (\frac{w+1}{{(w+1)}^{2}} - \frac{1}{{(w+1)}^{2}} )dw= \int_0^ \infty \frac{1}{{(w+1)}^{2}} d(w+1)^2 - \int_0^ \infty \frac{1}{{(w+1)}^{2}} dw = ln(w+1)^2|_{w=0}^{w= \infty } + \frac{1}{w+1}|_{w=0}^{w= \infty } = \infty

Hence, E( \frac{x}{y} ) doesn’t exist.
 
You are correct now.
 
Thank you again, Ray.
 

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