  Many existing procedures for detecting multiple change-points in data
sequences fail in frequent-change-point scenarios. This article proposes a new
change-point detection methodology designed to work well in both infrequent and
frequent change-point settings. It is made up of two ingredients: one is "Wild
Binary Segmentation 2" (WBS2), a recursive algorithm for producing what we call
a `complete' solution path to the change-point detection problem, i.e. a
sequence of estimated nested models containing $0, \ldots, T-1$ change-points,
where $T$ is the data length. The other ingredient is a new model selection
procedure, referred to as "Steepest Drop to Low Levels" (SDLL). The SDLL
criterion acts on the WBS2 solution path, and, unlike many existing model
selection procedures for change-point problems, it is not penalty-based, and
only uses thresholding as a certain discrete secondary check. The resulting
WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and
to significantly outperform the competition in the frequent change-point
scenarios tested. WBS2.SDLL is fast, easy to code and does not require the
choice of a window or span parameter.
