  While parametric multiple change point detection has been widely studied,
less attention has been given to the nonparametric task of detecting multiple
change points in a sequence of observations when their distribution is unknown.
Most existing work on this topic is either based on penalized cost functions
which can suffer from false positive detections, or on binary segmentation
which can fail to detect certain configurations of change points. We introduce
a new approach to change point detection which adapts the recently proposed
Wild Binary Segmentation (WBS) procedure to a nonparametric setting. Our
approach is based on the use of rank based test statistics which are especially
powerful at detecting changes in location and/or scale. We show via simulation
that the resulting nonparametric WBS procedure has favorable performance
compared to existing methods, particularly when it comes to detecting changes
in scale. We apply our procedure to study a problem in stylometry involving
change points in an author's writing style, and provide a full implementation
of our algorithm in an associated R package.
