  We study the discrepancy of jittered sampling sets: such a set $\mathcal{P}
\subset [0,1]^d$ is generated for fixed $m \in \mathbb{N}$ by partitioning
$[0,1]^d$ into $m^d$ axis aligned cubes of equal measure and placing a random
point inside each of the $N = m^d$ cubes. We prove that, for $N$ sufficiently
large, $$ \frac{1}{10}\frac{d}{N^{\frac{1}{2} + \frac{1}{2d}}} \leq \mathbb{E}
D_N^*(\mathcal{P}) \leq \frac{\sqrt{d} (\log{N})^{\frac{1}{2}}}{N^{\frac{1}{2}
+ \frac{1}{2d}}},$$ where the upper bound with an unspecified constant $C_d$
was proven earlier by Beck. Our proof makes crucial use of the sharp
Dvoretzky-Kiefer-Wolfowitz inequality and a suitably taylored Bernstein
inequality; we have reasons to believe that the upper bound has the sharp
scaling in $N$. Additional heuristics suggest that jittered sampling should be
able to improve known bounds on the inverse of the star-discrepancy in the
regime $N \gtrsim d^d$. We also prove a partition principle showing that every
partition of $[0,1]^d$ combined with a jittered sampling construction gives
rise to a set whose expected squared $L^2-$discrepancy is smaller than that of
purely random points.
