  We extend randomized jumplists introduced by Br\"onnimann et al. (STACS 2003)
to choose jump-pointer targets as median of a small sample for better search
costs, and present randomized algorithms with expected $O(\log n)$ time
complexity that maintain the probability distribution of jump pointers upon
insertions and deletions. We analyze the expected costs to search, insert and
delete a random element, and we show that omitting jump pointers in small
sublists hardly affects search costs, but significantly reduces the memory
consumption.
  We use a bijection between jumplists and "dangling-min BSTs", a variant of
(fringe-balanced) binary search trees for the analysis. Despite their
similarities, some standard analysis techniques for search trees fail for
dangling-min trees (and hence for jumplists).
