Bayes Minimax Estimation Let $S\sim B(n,\theta), l(\theta, a) = (\theta - a)^2,\delta=\bar{X} = S/n,$ and 
$$\delta^*(S)=\left(S+\frac{1}{2}\sqrt{n}\right)/\left(n+\sqrt{n}\right)$$
where $B(n,\theta)$ is Bernoulli distribution, $l(\theta, a)$ is loss function
How can I show that $\delta^*$ has constant risk and is Bayes for the beta, $\beta(\sqrt{n}/2, \sqrt{n}/2)$, prior. So, $\delta^*$ would be minimax. Also if we have $\theta \ne \frac{1}{2}$, would $lim_{n\rightarrow \infty}\left[R(\theta,\delta^*)/R(\theta,\delta)\right] > 1$? Otherwise, if $\theta = \frac{1}{2}$, would that make  $lim_{n\rightarrow \infty}\left[R(\theta,\delta^*)/R(\theta,\delta)\right] = 1$? How do I show this?
 A: The problem is fully processed by Wikipedia. As stated in my book,

Lemma 2.25$^0$$^1$ If $\delta_0$ is a Bayes estimator with respect to $\pi_0$ and if $R(\theta,\delta_0) \le r(\pi_0)$ for every $\theta$ in
$\Theta$, $\delta_0$ is minimax and $\pi_0$ is the least
favorable distribution.
Example 2.26 (Berger, 1985a) Consider $X\sim{\mathcal B}(n,\theta)$ when the probability $\theta$ is to be estimated under
the quadratic loss, $$ \mathrm L (\theta,\delta)  = 
 (\delta-\theta)^2. $$ Bayes estimators are then given by posterior
expectations and, when $$\theta \sim{\cal B}e \left({\sqrt{ n} \over
 2}, {\sqrt{ n} \over 2} \right)$$ the posterior mean$^2$ is $$\delta^
 \ast (x)  =  {x+ \sqrt{n}/2 \over n+ \sqrt{ n}}.$$ Moreover, this
estimator has constant risk$^3$, $$R(\theta,\delta^*) = 1/4(1+\sqrt{n})^2$$
Therefore, integrating out $\theta$, $$r(\pi) = R(\theta,\delta^*)$$ and
$\delta^*$  is minimax according to Lemma 2.25. Notice the difference
with the MLE, $\delta_0(x) = x/n$, for the small values of $n$, and
the unrealistic concentration of the prior around $0.5$ for larger
values of $n$.

[The Bayesian Choice, 2007, Chapter 2]

$^0$The statement is correcting for a typo on the range of $\theta$'s for which the inequality occurs: it should be the entire parameter set and not only the support of $\pi_0$, as shown by the counter-example of a Dirac mass prior in $\theta_0$, whose (null) risk $R(\theta_0,\delta_0)$ is equal to $r(\pi_0)$

$^1$The proof is not detailed in the book for being straightforward: if $R(\theta,\delta_0) \le r(\pi_0)$ for every $\theta$ in $\Theta$, then
$$r(\pi_0) = \int_\Theta R(\theta,\delta_0)\,\pi_0(\theta)\text d\theta$$
implies that $R(\theta,\delta_0) = r(\pi_0)$ almost everywhere on the support $\Omega$ of $\pi_0$. Now, if the estimator $\delta_0$ is not minimax, it means there exists an estimator $\delta_1$ such that
$$\sup_\theta R(\theta,\delta_1) < \sup_\theta R(\theta,\delta_0)=r(\pi_0)$$meaning that
$$\int_\Theta R(\theta,\delta_1)\,\pi_0(\theta)\text d\theta < r(\pi_0)$$
which exhibits a contradiction.

$^2$The posterior distribution is$$\theta|X=x \sim{\cal B}e \left(\frac{\sqrt{ n}}{2}+x, \frac{\sqrt{ n}}{2}+n-x \right)$$

$^3$The risk is obtained by$$R(\theta,\delta^*) = (\mathbb E_\theta[\delta^*(X)]-\theta)^2+\dfrac{\text{var}_\theta X}{(n+\sqrt{n}/2)^2}$$
