  The problem of quickest change detection in a sequence of independent
observations is considered. The pre-change distribution is assumed to be known,
while the post-change distribution is completely unknown. A window-limited
leave-one-out (LOO) CuSum test is developed, which does not assume any
knowledge of the post-change distribution, and does not require any post-change
training samples. It is shown that, with certain convergence conditions on the
density estimator, the LOO-CuSum test is first-order asymptotically optimal, as
the false alarm rate goes to zero. The analysis is validated through numerical
results, where the LOO-CuSum test is compared with baseline tests that have
distributional knowledge.
