How Can I Prove the Independence of Functions of Independent Random Variables?

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Discussion Overview

The discussion revolves around proving the independence of functions of independent random variables, specifically how to show that if X and Y are independent random variables, then U = g(X) and V = h(Y) are also independent. The scope includes theoretical aspects of probability, mathematical reasoning, and proof strategies.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant questions the meaning of U = g(X) and seeks clarification on the proof strategy for independence.
  • Another participant explains that U is a function of the random variable X and suggests looking at the joint distribution for continuous random variables.
  • A different participant attempts to derive the joint probability density function (pdf) for the continuous case and expresses uncertainty about their notation and whether they are on the right track.
  • One participant corrects a previous claim about the joint pdf, asserting that it should be f(X,Y) = g(x)h(y) rather than f[g(X),h(Y)] = g(x)h(y), and argues that U and V should be independent due to the independence of X and Y.

Areas of Agreement / Disagreement

Participants express differing views on the correct formulation of the joint pdf and the proof steps. There is no consensus on the proof's correctness, and uncertainty remains regarding the mathematical notation and approach.

Contextual Notes

Participants mention both continuous and discrete cases, indicating that the proof may depend on the type of random variables involved. There are unresolved issues with notation and the mathematical steps taken in the proof attempts.

franz32
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Hello, I need some help on the independence of random variables...
"How do I prove that if X and Y are two independent random variables, then U=g(X) and V = h(Y) are also independent?"

- Isn`t that if random variables X and Y are independent, it implies
that f(x,y) = g(x)h(y) and vice versa? Also, note that g(x) and h(y) are
two marginals. But what I don`t understand is that what does it mean to
have U = g(X) to be a capital "X"?

- {then U=g(X) and V = h(Y) are also independent} what am I supposed to
show in this proof? And lastly, what is my first step/strategy in proving
this? Hope you can give me hints.. =)
 
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U is g of the random variable X. You do this all the time, such as when working out the variance: it is
E(X^2)-E(X)^2, So there's a function of a random variable there (X^2).

Are these continuous of discrete R.V.'s? Not that it matters two much. If it 's continuous look at the pdf of the joind dist. since g is a function of X alone, and h a function of Y alone the double integrals INT dxdy split as int dx int dy. If discrete replace integrals with sums.
 
Sir matt grime/anyone... =]
I hope someone can guide me.
I want to prove first the continuous. So, the joint pdf can be described as

f[g(X),h(Y)] = INTaINTb g(x)h(y) dx dy -> am I right here?
where a and b are arbitrary intervals.
= INTa h(y) [INTb g(x)dx] dy -> h(y) is treated as a constant.
= [INTb g(x)dx] [INTa h(y)dy] -> [INTb g(x)dx] is now a constant
= g(X) h(Y)

I believe I got screwed up in my notations... is this the proof? I hope it is.. but can someone help me edit this... will I use u's and v's here?... I think not.

For the discrete case...

f(g(X), h(Y)) = P(U = g(X), V = h(Y)) = P(U = g(X)) P(V = h(Y)) = g(X)h(Y)?

Is this the right proof? I hope someone can help me.. =]
 
franz32 said:
f[g(X),h(Y)] = INTaINTb g(x)h(y) dx dy -> am I right here?
where a and b are arbitrary intervals.
= INTa h(y) [INTb g(x)dx] dy -> h(y) is treated as a constant.
= [INTb g(x)dx] [INTa h(y)dy] -> [INTb g(x)dx] is now a constant
= g(X) h(Y)

I believe you made a mistake here, it's not

[tex]f[g(X),h(Y)] = g(x) \cdot h(y)[/tex]

but it's

[tex] <br /> f(X,Y) = g(x) \cdot h(y)<br /> [/tex]

You were on the right track, but it should be

[tex]f(U,V) = g(U)h(V) = g(g(x)) \cdot h(h(y))[/tex]

I believe that U and V (g(x) and h(y), respectively) should be independent since Y cannot influence g(x) and X cannot influence h(y) since X and Y are independent. I just don't know how to prove it in mathematical notation, but it's worth a try =)
 
Last edited:

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