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

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If you have a question about this talk, please contact Mustapha Amrani .

Inference for Change-Point and Related Processes

We propose a Likelihood Ratio Scan Method (LRSM) for multiple change-points estimation in piecewise stationary processes. Using the idea of scan statistics, the computationally infeasible global multiple change-points estimation problem is reduced to a number of single change-point detection problems in various local windows. The computation can be performed efficiently with order $O(nlog n)$. Consistency for the estimated number and locations of the change-points are established. Moreover, a procedure for constructing confidence intervals for each of the change-point is developed. Simulation experiments show that LRSM outperforms other methods when the series length is large and the number of change-points is relatively small.

This talk is part of the Isaac Newton Institute Seminar Series series.

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