A pairwise likelihood-based approach for changepoint detection in multivariate time series models.
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.
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong , ralph.ma@hotmail.com.
Ting Fung
TF
Ma
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong , ralph.ma@hotmail.com.
Chun Yip
CY
Yau
Composite likelihood; Dynamic programming; Multiple changepoints; Structural break