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I need to find the distribution of $2\theta X_i^2$ in order to show that $\sum_{i=1}^n 2\theta X_i^2$ is a pivot, (and thus $\sum_{i=1}^n 2\theta X_i^2 \sim N(0,1)$). $X_1,X_2,...,X_n$ are i.i.d. with $f(x,\theta)=2x\theta e^{-\theta x^2}1(x>0)$ and $\theta >0$.

To find the distribution of $2\theta X_i^2$, I wanted to use the expected value and variance of $f(x,\theta)=2x\theta e^{-\theta x^2}1(x>0)$.

I found that $E[2\theta X_i^2]=2$ and $Var[2\theta X_i^2]=8-16\theta^2$ by integrating $f(x,\theta)$, however I am not sure this is the right method.

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    $\begingroup$ Add the self study tag. $\endgroup$ Commented Dec 13, 2017 at 22:35
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    $\begingroup$ Just apply the transformation $y=x^2$. You will find that $y$ has a very simple well-known distribution. You will not however be able to show that $\sum 2\theta X_i^2\sim N(0,1)$ because it is simply not true. $\endgroup$ Commented Dec 13, 2017 at 23:55
  • $\begingroup$ Thank you very much, I will do that. But how would I show that $\sum 2 \theta X_i^2$ is a pivot if it does not have $N(0,1)$ distribution? @GordonSmyth $\endgroup$ Commented Dec 14, 2017 at 0:40
  • $\begingroup$ Pivoting and normality have nothing at all to do with one another. A pivot is just a random variable whose distribution doesn't depend on $\theta$. There is no requirement for it to follow a normal, or any other, distribution. (Surely you could have found out for yourself, either from the course notes or from google en.wikipedia.org/wiki/Pivotal_quantity .) $\endgroup$ Commented Dec 14, 2017 at 18:28
  • $\begingroup$ $2\theta X^2_i$ is obviously a strictly positive quantity. I wonder how you expected to show that a strictly positive random variable follows a $N(0,1)$ distribution? That should have been ringing alarm bells for you. $\endgroup$ Commented Dec 14, 2017 at 18:58

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I show you if $X_1,X_2,...,X_n$ are i.i.d with $f(x;\theta)=2x\theta e^{-\theta x^2}\mathbb{1}(x>0)$ then what is the distribution of $X_i^2$ then you can go ahead by yourself.

Let $Y=X^2$

For $y \ge 0$, $F_Y(y)=P(Y<y)=P(X^2<y)=P(-\sqrt{y}<X<\sqrt{y})=P(0<X<\sqrt{y})\\=F_X(\sqrt{y})-F_X(0)=F_X(\sqrt{y})$

We take derivative on both sides. \begin{align*} f_Y(y)&=\frac{1}{2\sqrt{y}}f_X(\sqrt{y})\\&=\frac{1}{2\sqrt{y}}f_X(\sqrt{y})\\&=\frac{1}{2\sqrt{y}}(2\sqrt{y}\theta e^{-\theta y})\\&=\theta e^{-\theta y} \end{align*}

You can see $X_i^2$ has an exponential distribution.

Next, you need to find the sum of i.i.d exponential random variables; I think now you can go ahead by yourself.

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  • $\begingroup$ Thank you, the sum would be a gamma distribution. But since I am looking at $2\theta x_i^2=2\theta y_i$ how does the $2\theta$ change the distribution? $\endgroup$ Commented Dec 14, 2017 at 1:12
  • $\begingroup$ @Silvia ... If $W$ is gamma$(\alpha,\beta)$, what's $kW$? You don't seem to be contributing much of your own work here. $\endgroup$
    – Glen_b
    Commented Dec 14, 2017 at 1:15
  • $\begingroup$ I am sorry, I am asking questions to try to understand, not because I don't want to put thought into it, I really hope it doesn't seem that way. This is a hard subject for me, I wouldn't be asking questions if I knew how to do it myself :( @Glen_b $\endgroup$ Commented Dec 14, 2017 at 1:21
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    $\begingroup$ With this kind of study-based work, we're supposed to be offering hints; at the same time you would need to be checking your text, your notes, trying wikipedia, and so forth to take it along a step. If you have specific issues, ask about them, but you need to have some level of input or your question will probably close (perhaps as too broad, possibly as off topic). $\endgroup$
    – Glen_b
    Commented Dec 14, 2017 at 1:35

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