  Change point analysis is a statistical tool to identify homogeneity within
time series data. We propose a pruning approach for approximate nonparametric
estimation of multiple change points. This general purpose change point
detection procedure `cp3o' applies a pruning routine within a dynamic program
to greatly reduce the search space and computational costs. Existing
goodness-of-fit change point objectives can immediately be utilized within the
framework. We further propose novel change point algorithms by applying cp3o to
two popular nonparametric goodness of fit measures: `e-cp3o' uses E-statistics,
and `ks-cp3o' uses Kolmogorov-Smirnov statistics. Simulation studies highlight
the performance of these algorithms in comparison with parametric and other
nonparametric change point methods. Finally, we illustrate these approaches
with climatological and financial applications.
