BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Robust statistical decisions under model misspecification by re-we
 ighted Monte Carlo samplers - Holmes\, C (University of Oxford)
DTSTART:20140424T081500Z
DTEND:20140424T091500Z
UID:TALK52167@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Large complex data sets typically demand approximate models at
  some level of specification. In such situations it is important for the a
 nalyst to examine the robustness of conclusions to approximate predictions
 . Recent developments in optimal control and econometrics have established
  formal methods for linear quadratic state space models (see e.g. Hansen a
 nd Sargent 2008) by considering the local-minimax outcome within an inform
 ation divergence (Kullback-Leibler) neighbourhood around the approximating
  model. Here we show how these approaches can be extended to arbitrary pro
 bability models using Monte Carlo methods. We derive theoretical results e
 stablishing the uniqueness of the Kullback-Leibler criteria\, as well as B
 ayesian non-parametric methods to sample from the space of probability dis
 tributions within a fixed divergence constraint.\n
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR