Inference for Multiple Change-points in Linear and Non-linear Time Series Models

Wai Leong Ng; Shenyi Pan; Chun Yip Yau

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arXiv · 1703.00647

Inference for Multiple Change-points in Linear and Non-linear Time Series Models

Wai Leong Ng·Shenyi Pan·Chun Yip Yau

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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.

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Wai Leong Ng, Shenyi Pan, Chun Yip Yau. 2017-03-02. Inference for Multiple Change-points in Linear and Non-linear Time Series Models. https://arxiv.org/abs/1703.00647

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