Using CART to Detect Multiple Change Points in the Mean for Large Sample

A procedure is provided to detect multiple change-points in the mean for very large Gaussian signals. From an algorithmical point of view, visiting all possible configurations of change points cannot be performed on large samples. The proposed procedure runs CART to reduce the number of configurations of change-points by keeping the relevant ones, and then runs an exhaustive search on these change-points in order to obtain a convenient configuration. A simulation study compares the different algorithms in terms of theoretical performance and in terms of computational time.