(a) We consider the model $\{\operatorname{Poisson}(\theta): \theta \in(0, \infty)\}$ and an i.i.d. sample $X_{1}, \ldots, X_{n}$ from it. Compute the expectation and variance of $X_{1}$ and check they are equal. Find the maximum likelihood estimator $\hat{\theta}_{M L E}$ for $\theta$ and, using its form, derive the limit in distribution of $\sqrt{n}\left(\hat{\theta}_{M L E}-\theta\right)$.

(b) In practice, Poisson-looking data show overdispersion, i.e., the sample variance is larger than the sample expectation. For $\pi \in[0,1]$ and $\lambda \in(0, \infty)$, let $p_{\pi, \lambda}: \mathbb{N}_{0} \rightarrow[0,1]$,

$$k \mapsto p_{\pi, \lambda}(k)= \begin{cases}\pi e^{-\lambda} \frac{\lambda^{k}}{k !} & \text { for } k \geqslant 1 \\ (1-\pi)+\pi e^{-\lambda} & \text { for } k=0\end{cases}$$

Show that this defines a distribution. Does it model overdispersion? Justify your answer.

(c) Let $Y_{1}, \ldots, Y_{n}$ be an i.i.d. sample from $p_{\pi, \lambda}$. Assume $\lambda$ is known. Find the maximum likelihood estimator $\hat{\pi}_{M L E}$ for $\pi$.

(d) Furthermore, assume that, for any $\pi \in[0,1], \sqrt{n}\left(\hat{\pi}_{M L E}-\pi\right)$ converges in distribution to a random variable $Z$ as $n \rightarrow \infty$. Suppose we wanted to test the null hypothesis that our data arises from the model in part (a). Before making any further computations, can we necessarily expect $Z$ to follow a normal distribution under the null hypothesis? Explain. Check your answer by computing the appropriate distribution.

[You may use results from the course, provided you state it clearly.]