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SUMMARY:Eigenstructure in high dimensional random effects models - Iain Jo
 hnstone (Stanford University)
DTSTART:20180625T100000Z
DTEND:20180625T104500Z
UID:TALK107359@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The eigenstructure of i.i.d. samples from high dimensional dat
 a is known to show phenomena unexpected from experience with low dimension
 s -- eigenvalue spreading\, bias and eigenvector inconsistency. Motivated 
 by some problems in quantitative genetics\, we now consider high dimension
 al data with more than one level of variation\, and more specifically the 
 spectral properties of estimators of the various components of variance ma
 trices. We briefly describe bulk and edge eigenvalue distributions in this
  setting\, and then focus more attention on properties and estimation of &
 #39\;spiked&#39\; models.  This is joint work with Zhou Fan and Yi Sun. 
LOCATION:Seminar Room 1\, Newton Institute
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