Inference for multiple change points in time series via likelihood ratio scan statistics

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2016 · Volume 78 · Issue 4

Abstract

We propose a likelihood ratio scan method for estimating multiple change points in piecewise stationary processes. Using scan statistics reduces the computationally infeasible global multiple-change-point estimation problem to a number of single-change-point detection problems in various local windows. The computation can be efficiently performed with orderO{nptlog (n)}. Consistency for the estimated numbers and locations of the change points are established. Moreover, a procedure is developed for constructing confidence intervals for each of the change points. Simulation experiments and real data analysis are conducted to illustrate the efficiency of the likelihood ratio scan method.

Keywords

Change point detection, Confidence interval, Likelihood ratio, Piecewise stationary time series

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