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SUMMARY:Maximum likelihood estimation of a log-concave density - Oliver Fe
 ng (Statslab)
DTSTART:20190224T110500Z
DTEND:20190224T114000Z
UID:TALK120775@talks.cam.ac.uk
CONTACT:73969
DESCRIPTION:A density on R^d is said to be log-concave if its logarithm is
  a concave function\, and the estimation of a unknown log-concave density 
 based on i.i.d. observations represents a central problem in the area of n
 on-parametric inference under shape constraints. In contrast to traditiona
 l smoothing techniques\, the log-concave maximum likelihood estimator is a
  fully automatic estimator which does not require the choice of any tuning
  parameters and therefore has the potential to offer practitioners the bes
 t of the parametric and non-parametric worlds. I will discuss some recent 
 theoretical results on the performance of this estimator\, with a particul
 ar focus on its ability to adapt to structural features of the target dens
 ity.
LOCATION:Winstanley Lecture Theatre\, Trinity College
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