The distribution of ratio of two uniform variables

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The discussion centers on calculating the density of the ratio U = Y/X, where X and Y are independent uniform variables on (0,1). The initial calculation yields a density of 1/2, but this does not integrate to 1, indicating an error in the approach. Participants suggest using a double integral or integrating with respect to both variables to correctly derive the marginal distribution of U. Additionally, there is a query regarding the cumulative distribution function (CDF) of Z = min(X,Y), with a formula provided for its computation. The urgency of resolving these mathematical issues is emphasized, as one participant needs to submit their findings by Friday.
gimmytang
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Hello,
Let X ~ U(0,1), Y ~U(0,1), and independent from each other. To calculate the density of U=Y/X, let V=X, then:
f_{U,V}(u,v)=f_{X,Y}(v,uv)|v| by change of variables.
Then:
f_{U}(u)=\int_{0}^{1}{f_{X,Y}(v,uv)|v|dv}=\int_{0}^{1}{vdv}={1\over 2}, 0<u<\infty, which is not integrated to 1.
Where I am wrong?
gim :cry:
 
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you only integrated with respect to fu(u). Now you have to integrate with respect to fv(v). Or you could have just used a double integral to start with...
 
Actually the marginal distribution of U, namely the distribution of the ratio of two uniform variables, is the only thing that I am interested. To be more clear:
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 my result 1/2 is not a reasonable density since it's not integrated to 1.
gim :bugeye:
 
Question says Let X and Y be independent random variables with join cumulative distribution function (CDF) F subscript X,Y of (x,y)= P (X</= x, Y</=y). Show that the CDF Fz(z) of the random variable Z=min (X,Y) can be computed via
Fz(z)= Fx(z) + Fy(z) - Fx(z) . Fy(z) = 1 - (1 - Fx (z)) . (1- Fy (z))

Please reply to this asap. I need to submit this answer by Friday. Thanks!
 
electroissues said:
Question says Let X and Y be independent random variables with join cumulative distribution function (CDF) F subscript X,Y of (x,y)= P (X</= x, Y</=y). Show that the CDF Fz(z) of the random variable Z=min (X,Y) can be computed via
Fz(z)= Fx(z) + Fy(z) - Fx(z) . Fy(z) = 1 - (1 - Fx (z)) . (1- Fy (z))

Please reply to this asap. I need to submit this answer by Friday. Thanks!
Don't jump into the thread of another. What have you done so far?
 
Well, I'm new here and had problems starting a new thread.

I looked at PDF of an exponential function which is (1 - Fx (x)) and also since its also given its independent, we know it can be split into Fx (x) . Fy (y) but I can't put these things together.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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