Independence of Random Variables

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Homework Help Overview

The discussion revolves around the independence of random variables defined by a joint probability density function, specifically ##f_{X,Y}(x,y)=2e^{-x}e^{-y}\ ;\ 00##. Participants are examining the implications of a theorem regarding independence and the conditions under which it holds.

Discussion Character

  • Conceptual clarification, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • Participants explore the definitions of independence and the conditions under which the theorem applies. There is a focus on the marginal densities and the implications of the joint density being defined only in a specific region.

Discussion Status

Some participants have provided guidance on computing marginal densities and questioning the assumptions regarding the intervals for independence. There is an ongoing exploration of whether the joint density can be expressed as a product of marginal densities across the entire defined space.

Contextual Notes

There is a noted contradiction in the values of the constant ##k## derived from the marginal densities, raising questions about the completeness of the theorem in the context of the defined intervals for the random variables.

showzen
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Homework Statement


Given ##f_{X,Y}(x,y)=2e^{-x}e^{-y}\ ;\ 0<x<y\ ;\ y>0##,
The following theorem given in my book (Larsen and Marx) doesn't appear to hold.

Homework Equations


Definition
##X## and ##Y## are independent if for every interval ##A## and ##B##, ##P(X\in A \land Y\in B) = P(X\in A)P(Y\in B) ##.
Theorem
##X## and ##Y## are independent iff ##f_{X,Y}(x,y)=g(x)h(y)##.
If so, there is a constant ##k## such that ##f_X(x)=kg(x)## and ##f_Y(y)=(1/k)h(y)##.

The Attempt at a Solution


Consider ##g(x)=2e^{-x}## and ##h(y)=e^{-y}##. Then, ##f_{X,Y}(x,y)=g(x)h(y)##, therefore theorem indicates that ##X## and ##Y## are independent.
The constant ##k## is
##k=\int_0^\infty h(y)dy=1##
##k=\int_0^y g(x)dx = 2(1-e^{-y})##
There is a contradiction in the value of k and it is not constant.

Am I missing something, or is the theorem incomplete in that it is lacking details on the intervals that the random variables are defined on?
 
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The theorem is correct, you should have: ##f_X(x) = \int_x^\infty f_{X,Y}(x,y)dy## and ##f_Y(y) = \int_0^y f_{X,Y}(x,y)dx##.
 
showzen said:

Homework Statement


Given ##f_{X,Y}(x,y)=2e^{-x}e^{-y}\ ;\ 0<x<y\ ;\ y>0##,
The following theorem given in my book (Larsen and Marx) doesn't appear to hold.

Homework Equations


Definition
##X## and ##Y## are independent if for every interval ##A## and ##B##, ##P(X\in A \land Y\in B) = P(X\in A)P(Y\in B) ##.
Theorem
##X## and ##Y## are independent iff ##f_{X,Y}(x,y)=g(x)h(y)##.
If so, there is a constant ##k## such that ##f_X(x)=kg(x)## and ##f_Y(y)=(1/k)h(y)##.

The Attempt at a Solution


Consider ##g(x)=2e^{-x}## and ##h(y)=e^{-y}##. Then, ##f_{X,Y}(x,y)=g(x)h(y)##, therefore theorem indicates that ##X## and ##Y## are independent.
The constant ##k## is
##k=\int_0^\infty h(y)dy=1##
##k=\int_0^y g(x)dx = 2(1-e^{-y})##
There is a contradiction in the value of k and it is not constant.

Am I missing something, or is the theorem incomplete in that it is lacking details on the intervals that the random variables are defined on?

You are definitely missing something: your "product" formula for ##f_{X,y}(x,y) ## holds only over the region ##0 < x < y##. (Presumably, ##f_{X,Y}## is zero outside that region, but the question does not say that.) Anyway, a bivariate density for a pair of independent random variables would need to be non-zero on the whole of the bivariate space ## 0 < x,y < \infty##.

To see explicitely that ##X,Y## are NOT independent, compute the marginal densities
$$ f_X(x) = \int_{-\infty}^{\infty} f_{X,Y}(x,y) \, dy = \int_x^{\infty} e^{-x} e^{-y} \, dy$$
and
$$f_Y(y) = \int_{-\infty}^{\infty} f_{X,Y}(x,y) \, dx =\int_0^y e^{-x} e^{-y} \, dx .$$
Finish the calculations and then check if ##f_X(x) f_Y(y)## agrees with your ##f_{X,Y} (x,y)##
 
Last edited:
##f_X(x) = 2\int_x^\infty e^{-x}e^{-y}dy = 2e^{-2x}##
##f_Y(y) = 2\int_0^y e^{-x}e^{-y}dx = 2e^{-y}(1-e^{-y})##
##f_X(x)f_Y(y) = 4e^{-2x}e^{-y}(1-e^{-y}) \neq f_{X,Y}(x,y)##
So we can show explicitly that they are not independent...
But we can separate ##f_{X,Y}(x,y)## into a product of ##g(x)## and ##h(y)## which implies independence by the theorem?
 
showzen said:
##f_X(x) = 2\int_x^\infty e^{-x}e^{-y}dy = 2e^{-2x}##
##f_Y(y) = 2\int_0^y e^{-x}e^{-y}dx = 2e^{-y}(1-e^{-y})##
##f_X(x)f_Y(y) = 4e^{-2x}e^{-y}(1-e^{-y}) \neq f_{X,Y}(x,y)##
So we can show explicitly that they are not independent...
But we can separate ##f_{X,Y}(x,y)## into a product of ##g(x)## and ##h(y)## which implies independence by the theorem?

No, no, no! Absolutely not! Your ##f_{X,Y}(x,y)## is NOT a product of some ##g(x)## and some ##h(y)## over the whole quarter plane ##0 < x, y < \infty##. It is a product, but only over the restricted region ##0 < x < y < \infty##, and that fact makes ##X## and ##Y## very definitely not independent. In fact, you should do what I suggested in my first response, which is to compute the marginals ##f_X(x)## and ##f_Y(y)##. You can use these to compute ##P(X \in A)## and ##P(Y \in B)## for ##A = \{ 0 < x < 1 \}## and ##B= \{ 0 < y < 2 \}##. Now compute ##P(X \in A) \cdot P(Y \in B)##. Finally, compute ##P(X \in A \; \& \; Y \in B)## by integrating ##f_{X,Y}(x,y)## over the appropriate 2-dimensional region. Do you get different answers? (I do.)
 
Yes, the answers are different. Can ##X## and ##Y## only be independent if they are defined over a rectangle?
 
showzen said:
Yes, the answers are different. Can ##X## and ##Y## only be independent if they are defined over a rectangle?

Basically, yes, and for obvious reasons: you cannot make ##g(x) h(y)## come out at zero over a part of the ##(x,y)## plane where both ##g(x) \neq 0## and ##h(y) \neq 0##.
 
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