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SUMMARY:Multiple change-point estimation in high-dimensional Gaussian grap
 hical models - Sandipan Roy (University College London)
DTSTART:20180503T100000Z
DTEND:20180503T110000Z
UID:TALK104899@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:We consider the consistency properties of a regularised estima
 tor for the simultaneous identification of both changepoints and graphical
  dependency structure in multivariate time-series. Traditionally\, estimat
 ion of Gaussian Graphical Models (GGM) is performed in an i.i.d setting. M
 ore recently\, such models have been extended to allow for changes in the 
 distribution\, but only where changepoints are known a-priori. In this wor
 k\, we study the Group-Fused Graphical Lasso (GFGL) which penalises partia
 l-correlations with an L1 penalty while simultaneously inducing block-wise
  smoothness over time to detect multiple changepoints. We present a proof 
 of consistency for the estimator\, both in terms of changepoints\, and the
  structure of the graphical models in each segment. Several synthetic expe
 riments and a real data application validate the performance of the propos
 ed methodology.<br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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