Inference for Multiple Change Points in Time Series via Likelihood Ratio Scan Statistics
提出一种似然比扫描方法，将多个变点估计问题转化为局部窗口内的单变点检测，计算复杂度为O(n log n)，并给出变点数量和位置的一致性估计及置信区间。
Summary 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 order O{npt log (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.