Supremum of a stochastic process Let $(x_1,...,x_N)$ be points in $R^d$, and $\sigma=(\sigma_1,...,\sigma_N)$ are i.i.d. Rademacher variables (+1 or -1 with probability 0.5 each). (Or alternatively, $\sigma$ could be a standard Gaussian vector.) Also let $z$ in an infinite subset of $R^d$, and $\|\cdot\|_2$ will denote the Euclidean norm. 
I am looking for a good upper estimate of:
$E_{\sigma} \sup_z \| \sum_{n=1}^N \sigma_n \frac{x_n-z}{\|x_n-z\|_2} \|_2$.
So far I only got the upper bound of $N$ (by triangle inequality), and the lower bound $\sqrt{N}$ by Jensen inequality, and I would like to know if there is a way to improve on the upper estimate or the lower estimate? 
If not, are there conditions (on the domain of $z$) under which the upper bound would reduce to ${\mathcal O}(\sqrt{N})$?
Edit: The points $x_1,...,x_N$ are assumed to be in general position (in a ball of radius $R$). 
Edit2: The domain of values of $z$ is an infinite set in $R^d$. Can be assumed to be bounded.
Edit3: The domain of $z$ is allowed to depend on $N$, $R$ (or on other assumed characteristics of the point set), but not on the individual points.
Edit4: I also want to avoid dependence on $d$.
 A: First, if the subset $Z$ of $R^d$ of allowed values for $z$ is a singleton, then writing $e_n=(x_n-z)/\|x_n-z\|_2=(e_{n,j})_{j=1}^d$, we have
$$E_\sigma \left\|\sum\sigma_ne_n\right\|_2=E_\sigma\sqrt{\sum_j \left(\sum_n\sigma_ne_{n,j}\right)^2}\le\sqrt{E_\sigma\sum_j\left(\sum_n\sigma_ne_{n,j}\right)^2}$$
$$%=\sqrt{\sum_jE_\sigma\left[\left(\sum_n\sigma_ne_{n,j}\right)^2\right]}
=\sqrt{\sum_j\mathrm{Var}_\sigma\left[\sum_n\sigma_ne_{n,j}\right]}
=\sqrt{\sum_j\sum_n\mathrm{Var}_\sigma\left[\sigma_ne_{n,j}\right]}$$
$$=\sqrt{\sum_j\sum_ne_{n,j}^2}=\sqrt{\sum_n \|e_n\|_2^2}=\sqrt{N}.$$
Second, if $Z$ is allowed to depend on $N$, then let $Z$ be of very small diameter $\delta>0$, relative to


*

*the distances $\|x_i-x_j\|_2$,

*$N$, and

*the modulus of continuity of the function $z\mapsto \left\| \sum \sigma_n e_n\right\|_2$,


then we should be able to get
for a fixed $z_0\in Z$, and for all $\sigma$,
$$\sup_{z\in Z}\left\|\sum\sigma_ne_n\right\|_2 \le \left\|\sum\sigma_ne_n\text{ (for $z_0$)}\right\|_2 + \epsilon$$
and hence
$$E_\sigma \left(\sup_z\left\|\sum\sigma_ne_n\right\|_2\right) \le E_\sigma\left( \left\|\sum\sigma_ne_n\text{ (for $z_0$)}\right\|_2 + \epsilon\right) =
 \mathcal O(\sqrt{N}).$$
