Suppose that $X_1, \ldots X_n$ are iid $N(\mu, \sigma^2)$ where $\mu$ and $\sigma$ are both unknown with $\mu \in \mathbb{R}$, $\sigma \in \mathbb{R}+$, $\boldsymbol{\theta} = (\mu, \sigma)$, $n \geq 2$. Consider the parametric function $\tau (\boldsymbol{\theta}) = e^{2(\mu + \sigma^2)}$. Derive the UMVUE for $\tau(\theta)$.
Solution:
We can show $\boldsymbol{T} = (\bar{X}_n, S^2_n)$ is complete sufficient, where $\bar{X}_n = \sum_{i=1}^{n} \frac{1}{n} X_i$ is the sample mean and $S^2_n = (n-1)^{-1} \sum_{i=1}^{n} (X_i - \bar{X})^2$ is the sample variance. I think best approach here is use $T = e^{2 X_1}$, so $E(T) = \tau(\theta)$, i.e., $T$ is unbiased. Then by the Lehmann-Scheffe Theorem I (link, pg. 371), the UMVUE is \begin{equation} E_{\theta}[e^{2 X_1} | \bar{X}_n, S^2_n] \end{equation}
Can we simplify this further? Is there another approach to try? Thanks.