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Oct 29, 2020 at 17:55 comment added Octavius Understood. Thank you very much.
Oct 29, 2020 at 17:55 vote accept Octavius
Oct 29, 2020 at 16:23 history edited tommik CC BY-SA 4.0
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Oct 29, 2020 at 16:15 comment added tommik @Octavius : ahah ... I edited the answer again with more and more details...your formula is correct but with non monotonic transformation it must be updated. in general you can pass via CDF...it is the general method. I wrote down the case of $|X|$. Reading the text you posted, they ask you the distribution function, thus my method is faster.
Oct 29, 2020 at 16:10 history edited tommik CC BY-SA 4.0
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Oct 29, 2020 at 16:05 comment added Octavius I see, so my CDF is $$F(x) = \frac{x}{2\sqrt{1-y_0^2}} + \frac{1}{2} \quad .$$ But how do I get the CDF for the random variables $x^4$, $|x|$, $-x$? The "transformation law" for the densities would be $$g(\tilde{x}) = \Big| \frac{\mathrm{d}h^{-1}}{\mathrm{d}\tilde{x}} \Big| \cdot f\big(h^{-1}(\tilde{x})\big)$$ where $f(x)$ is the density of the variable $X$, and $g(\tilde{x})$ is the density of the variable $\tilde{X} = h(X)$. With constant density as written above I would have density zero for the other variables.
Oct 29, 2020 at 15:50 comment added tommik @Octavius : in the meantime you were writing the comment I edited my answer with more details. The fact that in your conditional density there is no $X$ is better for you....it's a Uniform density in the support that I showed. The X will appear as soon as you calculate the CDF
Oct 29, 2020 at 15:48 history edited tommik CC BY-SA 4.0
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Oct 29, 2020 at 15:42 history edited tommik CC BY-SA 4.0
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Oct 29, 2020 at 15:42 comment added Octavius In this case I have to calculate: $$ f(x|y=y_0) = \frac{f(x,y)}{\tilde{f}(y)}$$, which is $$f(x|y=y_0) = \frac{1}{2\sqrt{1-y_0^2}}$$. But this term is $x$-independent. Wouldn't that mean, that the random variables $x^4$, $|x|$ and $-x$ have a density function of zero everywhere?
Oct 29, 2020 at 15:18 history answered tommik CC BY-SA 4.0