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SUMMARY:Sequential detection of structural changes in irregularly observed
  data - Tobias Kley (Humboldt-Universität zu Berlin)
DTSTART:20180405T100000Z
DTEND:20180405T110000Z
UID:TALK103420@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Online surveillance of time series is traditionally done with 
 the aim to identify changes in the marginal distribution under the assumpt
 ion that the data between change-points is stationary and that new data is
  observed at constant frequency. In many situations of interest to data an
 alysts\, the classical approach is too restrictive to be used unmodified. 
 We propose a unified system for the monitoring of structural changes in st
 reams of data where we use generalised likelihood ratio-type statistics in
  the sequential testing problem\, obtaining the flexibility to account for
  the various types of changes that are practically relevant (such as\, for
  example\, changes in the trend of the mean). The method is applicable to 
 sequences where new observations are allowed to arrive irregularly. Early 
 identification of changes in the trend of financial data can assist to mak
 e trading more profitably. In an empirical illustration we apply the proce
 dure to intra-day prices of components of the NASDAQ-100 stock market inde
 x. This project is joint work with Piotr Fryzlewicz.  <br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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