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SUMMARY:Inference for multiple change-points in time series via likelihood
  ratio scan statistics - Yao\, C-Y (Chinese University of Hong Kong)
DTSTART:20140115T140000Z
DTEND:20140115T143000Z
UID:TALK49918@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION: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 chang
 e-points estimation problem is reduced to a number of single change-point 
 detection problems in various local windows. The computation can be perfor
 med efficiently with order $O(nlog n)$. Consistency for the estimated numb
 er and locations of the change-points are established. Moreover\, a proced
 ure 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 relati
 vely small.\n
LOCATION:Seminar Room 1\, Newton Institute
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