What is this well known probability distribution?

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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