  Very long and noisy sequence data arise from biological sciences to social
science including high throughput data in genomics and stock prices in
econometrics. Often such data are collected in order to identify and understand
shifts in trend, e.g., from a bull market to a bear market in finance or from a
normal number of chromosome copies to an excessive number of chromosome copies
in genetics. Thus, identifying multiple change points in a long, possibly very
long, sequence is an important problem. In this article, we review both
classical and new multiple change-point detection strategies. Considering the
long history and the extensive literature on the change-point detection, we
provide an in-depth discussion on a normal mean change-point model from aspects
of regression analysis, hypothesis testing, consistency and inference. In
particular, we present a strategy to gather and aggregate local information for
change-point detection that has become the cornerstone of several emerging
methods because of its attractiveness in both computational and theoretical
properties.
