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

Date: 
Wednesday 15th January 2014 - 14:00 to 14:30
Venue: 
INI Seminar Room 1
Abstract: 
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 $O(n\log n)$. 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.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons