A pairwise likelihood-based approach for changepoint detection in multivariate time series models
提出一种基于成对似然和最小描述长度的方法，用于估计多变量时间序列中变点的数量和位置，并在每个分段中进行模型选择，通过剪枝动态规划算法高效计算。
This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.