内容简介: 
In this talk, we present a procedure for detecting multiple changepoints in a meanshift model, where the number of changepoints is allowed to increase with the sample size. A theoretic justification for this method is also given. We first convert the changepoint problem into a variable selection problem by partitioning the data sequence into several segments. Then, we apply a modified variance inflation factor regression algorithm to each segment in sequential order. When a segment that is suspected of containing a changepoint is found, we use a weighted cumulative sum to test if there is indeed a changepoint in this segment. The procedure is implemented in an algorithm which, compared to two popular methods via simulation studies, demonstrates satisfactory performance in terms of accuracy, stability and computation time. Real data examples are also provided.
