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SUMMARY:Modern advances in likelihood ratio inference - Moulinath Banerjee
  (University of Michigan\, Ann Arbor\, USA)
DTSTART:20120530T153000Z
DTEND:20120530T163000Z
UID:TALK38418@talks.cam.ac.uk
CONTACT:27212
DESCRIPTION: Since Wilks' seminal 1938 paper which demonstrated the conver
 gence of the\nlikelihood ratio statistic in a parametric model to a limiti
 ng chi-squared distribution\,\nlikelihood ratios have been used extensivel
 y for inferential purposes\, not least owing\nto the fact the they are asy
 mptotically pivotal. In this presentation\, I will highlight\nsome of the 
 modern developments in the theory of likelihood ratio inference where\nasy
 mptotic pivotality continues to be preserved\, focusing mainly on likeliho
 od ratio\ninference on differentiable functionals in semiparametric models
  and pointwise\nlikelihood ratios in shape constrained estimation.
LOCATION:MR2\, CMS
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