  We introduce a class of convex equivolume partitions. Expected star
discrepancy results are compared for stratified samples under these partitions,
including simple random samples. There are four main parts of our results.
First, among these newly designed partitions, there is one that minimizes the
expected star discrepancy, thus we partly answer an open question in [F.
Pausinger, S. Steinerberger, J. Complex. 2016]. Second, there are an infinite
number of such class of partitions, which generate point sets with smaller
expected discrepancy than classical jittered sampling for large sampling
number, leading to an open question in [M. Kiderlen, F. Pausinger, Monatsh.
Math. 2021] being solved. Third, we prove a strong partition principle and
generalize the expected star discrepancy under these partition models from
$L_2-$discrepancy to star discrepancy, hence an open question in [M. Kiderlen,
F. Pausinger, J. Complex. 2021] is answered. In the end, optimal expected star
discrepancy upper bound under this class of partitions is given, which is
better than using jittered sampling.
