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SUMMARY:Cluster-Seeking Shrinkage Estimators - Dr K Pavan Srinath\, CUED
DTSTART:20160706T100000Z
DTEND:20160706T110000Z
UID:TALK66768@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:We consider the problem of estimating a high-dimensional vecto
 r of parameters from a noisy one-time observation. The noise is iid Gaussi
 an with known variance\, and the performance of the estimator is measured 
 via squared-error loss. For this problem\, shrinkage estimators\, which sh
 rink the observed data towards a point or a target subspace\, have been po
 pular because they dominate the simple maximum-likelihood estimator (when 
 the number of dimensions exceeds two).  \n   In this talk\, we review the 
 key aspects of shrinkage estimation\, and then introduce shrinkage estimat
 ors that use the data to determine a "good" target subspace to shrink the 
 data towards. We give concentration results for the squared-error loss and
  convergence results for the risk of the proposed estimators. We also pres
 ent simulation results that validate the theory.
LOCATION:SigProC seminar room (3rd floor of Dept. of Engineering)
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