Suppose I have a sample from a multinomial distribution $\text{Multi}(p_1,\ldots,p_k)$, where $p_1+\dots+p_k=1$. I'd like simultaneous confidence intervals for the $p_i$'s. That is, for any $\alpha\in(0,1)$, I want to define $\ell_i$'s and $u_i$'s (random variables depending on the random sample) such that $$ P(p_1\in [\ell_1,u_1],\ldots,p_n\in[\ell_n,u_n])\ge 1-\alpha, $$ and obviously the closer the left hand side is to $1-\alpha$, the better. The special consideration is that the number of classes won't be too small (e.g. 10-100 classes), so I'm wondering if there's something significantly better than a Bonferroni approach.
Thanks!