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DESCRIPTION:High-dimensional changepoint estimation via sparse projection\n
 \nAbstract \n\n\n	\n		\n			\n				Changepoints are a very common feature of Big Data 
 that arrive in the form of a data stream. We study high-dimensional time s
 eries in which\, at certain time points\, the mean structure changes in a 
 sparse subset of the coordinates. The challenge is to borrow strength acro
 ss the coordinates in order to detect smaller changes than could be observ
 ed in any individual component series. We propose a two-stage procedure ca
 lled 'inspect' for estimation of the changepoints: first\, we argue that a
  good projection direction can be obtained as the leading left singular ve
 ctor of the matrix that solves a convex optimisation problem derived from 
 the CUSUM transformation of the time series. We then apply an existing uni
 variate changepoint detection algorithm to the projected series. Our theor
 y provides strong guarantees on both the number of estimated changepoints 
 and the rates of convergence of their locations\, and our numerical studie
 s validate its highly competitive empirical performance for a wide range o
 f data generating mechanisms.\n			\n		\n	\n\n
DTSTART:20161201T203000Z
DTEND:20161201T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Richard Samworth  University of Cambridge
URL:/mathstat/channels/event/richard-samworth-universi
 ty-cambridge-264546
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