I was studying the method of moments estimation of parameters, and I encountered the following problem.
I have a geometric distribution as following:
$P(X=k) = p(1-p)^{k-1}$, and a sample size of n, all observations are independent
Hence, it can be quite easily shown that
$\hat p = \dfrac{1}{\overline{X}}$, where $\overline{X}$ is the sample mean, this is the MME estimation of p.
Now, if I want to find the approximate normal distribution for $\hat p$, I would have to find out the variance of $\hat p$
Since, $\hat p = \dfrac{n}{\sum\limits_{i=1}^n X_n}$, we see that the bottom follows a negative binomial distribution $NegativeBin(n, p)$.
Now I am stuck at this step, and I can't figure out how to calculate the expression for the PMF and the variance of $\hat p$.
Thank you very much!
