Help on the density of Y/X, where X,Y~U(0,1)

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The discussion centers on finding the density of the ratio Y/X, where X and Y are independent and identically distributed uniform random variables on (0,1). The initial attempt yielded a marginal density of 1/2, which raised concerns about normalization. A participant clarified that when integrating the joint distribution, the limits depend on the other variable, leading to a corrected density function. The final density is defined as 0.5 for u in [0,1) and 0.5u^(-2) for u in [1,∞), confirming that the distribution integrates to 1. The importance of correctly determining the variable's domain in relation to the other variable was emphasized.
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Hi,
There are two i.i.d uniform random variables X and Y. Now I need to know the density of Y/X. My method is like this:
Let U=Y/X, V=X. Then the marginal density of U is what I need.

f_{U}(u)={\int_{-\infty}^{\infty}f_{U,V}(u,v)dv}={\int_{0}^{1}f_{X,Y}(u,uv)|v|dv}={\int_{0}^{1}vdv}=1/2
Now the question is that my result 1/2 is not a reasonable density since it's not integrated to 1. Can anyone point out where I am wrong?
gim :bugeye:
 
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I'm really rusty on this stuff, too rusty too even formalize a proper answer without brushing up on notation etc. So excuse me if this answer is a little vague but I think I know roughly what your problem is.

When you integrate out one of the variables of the joint distribution the limit is not just a simple "1", its actually function of the other variable. It helps if you try to sketch the joint distribution, the way I'm picturing it you should end up with something like,

f(u) = int(v,dv,0..1) : u in [0..1)
and
f(u) = int(v,dv,0..1/u) : u in [1..infinity)

This gives

f(u) = 0.5 : u in [0..1)
and
f(u)=0.5 u^(-2) : u in [1..infinity)

I'm not 100% sure it's correct but it looks reasonable
 
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Thank you for your answer. It's correct since the distribution function is integrated to 1. I always have some confusion about the right domain of the variable since it's a function of the other variable.
gim
 
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