Statistics Seminar: Chris Lloyd -- Computing Highly Accurate Confidence Limits from Discrete data using Importance Sampling

Chris J.  Lloyd Melbourne Business School University of Melbourne 

Location: Carslaw 273 

Time: 2pm Friday, May 18, 2012 

Title: Computing Highly Accurate Confidence Limits from Discrete data using Importance
Sampling 

Abstract: For discrete data, confidence limits based on a normal approximation to
standard likelihood based pivotal quantities can perform poorly even for quite large
sample sizes.  To construct exact limits requires the probability of a suitable tail set
as a function of the unknown parameters.  In this paper, importance sampling is used to
estimate this surface and hence the confidence limits.  The technology is simple and
straightforward to implement.  Unlike the recent methodology of Garthwaite and Jones
(2009), the new method allows for nuisance parameters; is an order of magnitude more
efficient than the Robbins-Monro bound; does not require any simulation phases or tuning
constants; gives a straightforward simulation standard error for the target limit;
includes a simple diagnostic for simulation breakdown.