Title: Inference for Multiple Change-points in Linear and Non-linear Time Series Models,
Abstract: In this paper we develop a generalized likelihood ratio scan method (GLRSM)
for multiple change-points inference in piecewise stationary time series, which
estimates the number and positions of change-points and provides a confidence
interval for each change-point. The computational complexity of using GLRSM for
multiple change-points detection is as low as $O(n(\log n)^3)$ for a series of
length $n$. Consistency of the estimated numbers and positions of the
change-points is established. Extensive simulation studies are provided to
demonstrate the effectiveness of the proposed methodology under different
scenarios.