What is this well known probability distribution?

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Homework Help Overview

The discussion revolves around a probability density function defined for a random variable X and its transformation to another variable Y. The original poster seeks to identify the well-known probability distribution that corresponds to the derived density function for Y.

Discussion Character

  • Exploratory, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • The original poster attempts to derive the probability density function for Y by substituting expressions for X and exploring the relationship between the two variables. Some participants question the correctness of the transformation method used, suggesting that the relationship between the densities requires careful consideration of the differential elements. Others explore the application of the fundamental theorem of calculus to derive the density function for Y.

Discussion Status

Participants are actively engaging with the problem, offering insights into the transformation of variables and the implications of the fundamental theorem of calculus. There is a recognition of the exponential distribution as a potential outcome, although some uncertainty remains regarding the correctness of the approaches taken.

Contextual Notes

There appears to be some confusion regarding the transformation of the probability density functions and the assumptions about the intervals corresponding to the variables X and Y. The original poster expresses uncertainty about their initial attempts and the validity of the results obtained.

Avatrin
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Homework Statement


I have a probability density function:
f[itex]_{X}[/itex](x) = θk[itex]^{θ}[/itex]x[itex]^{-θ-1}[/itex] when x > k and 0 everywhere else
And:
Y = θlog(X/k)

Find the probability density function for Y. What well known density function is this?

Homework Equations


I don't really know where to start...

The Attempt at a Solution


I first tried solving for x and:
X = ke[itex]^{Y/θ}[/itex]
Plugging this into f(x):
f[itex]_{Y}[/itex](y) = θk[itex]^{θ}[/itex](ke[itex]^{y/θ}[/itex])[itex]^{-θ-1}[/itex]

I also tried going the other way around:
f[itex]_{Y}[/itex](x) = θlog(θk[itex]^{θ-1}[/itex]x[itex]^{-θ-1}[/itex])

But, I don't really know what to do and where to start.. ...
 
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You can't simply rewrite fX(x) in terms of Y the way you did. It's a little more complicated. The correct relationship is
$$f_X(x)\,dx = f_Y(y)\,dy$$ where I've assumed both dx and dy are positive. The idea here is that interval (x, x+dx) corresponds to the interval (y, y+dy) so the probability that X is in the interval (x, x+dx), which is given by the lefthand side of the equation, should be equal to the probability Y is in the interval (y, y+dy), which is given by the righthand side of the equation. Solving for fY(y), you get
$$f_Y(y) = \frac{f_X(x)}{|dy/dx|}.$$ The absolute value is necessary to cover the case where dx>0 corresponds to dy<0. That is, when y decreases as x increases.
 
Actually, I had to use the fundamental theorem of calculus:

[itex]\frac{d}{dy}[/itex]∫[itex]^{y}_{0}[/itex]f(x)dx = f(y)

Therefore;
[itex]\frac{d}{dy}[/itex]∫[itex]^{ke^(Y/θ)}_{0}[/itex]θk[itex]^{θ}[/itex]x[itex]^{-(θ+1)}[/itex]dx
= [itex]\frac{d}{dy}[/itex]-e[itex]^{-y}[/itex]
= e[itex]^{-y}[/itex]
And, this is the exponential distribution..

Or... Am I wrong again?
 
Avatrin said:
Actually, I had to use the fundamental theorem of calculus:

[itex]\frac{d}{dy}[/itex]∫[itex]^{y}_{0}[/itex]f(x)dx = f(y)

Therefore;
[itex]\frac{d}{dy}[/itex]∫[itex]^{ke^(Y/θ)}_{0}[/itex]θk[itex]^{θ}[/itex]x[itex]^{-(θ+1)}[/itex]dx
= [itex]\frac{d}{dy}[/itex]-e[itex]^{-y}[/itex]
= e[itex]^{-y}[/itex]
And, this is the exponential distribution..

Or... Am I wrong again?

No, you are now correct.

RGV
 

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