Deriving the asymptotic distribution using delta method I have the density function:
$P_Y(y) = \sqrt{\frac{1}{2\pi y^3}} \exp\left(-\frac{(y-\mu)^2}{2\mu^2y}\right)$
If we define $r := \mu^2$ what is its asymptotic distribution?
The right answer is $\sqrt{n}(\hat{r} - r) \sim N(0, 4r^{5/2})$ but I don't understand how i should get there.
I know that $E[Y] = \mu$ and $V[Y] = \mu^3$
And for $P_Y$ I know that the ML-estimation for $\mu$ is just $\hat{\mu} = \bar{y}$ and the asymptotic distribution for that ML-estimation is $\sqrt{n}(\hat{\mu} - \mu) \sim N(0, \mu^3)$
In this case I've calculated the Fisher information to be: $I(\theta_n) = -E[\ell_n''(\mu)] = \frac{n}{\mu^3}$ and this seems to be right.
I've used the formula $\sqrt{n}(\hat{\theta} - \theta) \sim N(0, \frac{1}{I(\theta)})$, where $I(\theta)$ is the Fisher information. But this does not yield the right answer, at least not how I try to use it when we define $r$ to be $r := \mu^2$
But I am now stuck. I think I've written down the relevant information.
EDIT 1
The ML-estimation for $r$ is $\hat{r} = (\hat{\mu})^2$

Consider $\mathop{g}\left(x\right)\mathrel{:=x^2}$ with derivative $\mathop{g'}\left(x\right)=2x$. What does the Wikipedia article on the delta method tell you about the asymptotic distribution of $\sqrt{n}\left[\mathop{g}\left(\hat\mu\right)-\mathop{g}\left(\mu\right)\right]$?1

1 You can ignore the assumption $g'(x) \neq 0$ if you allow for singular normal limits.
