SMS scnews item created by Andrew Mathas at Thu 10 Oct 2013 1615
Expiry: 25 Oct 2013
Calendar1: 25 Oct 2013 1200-1300
CalLoc1: AGR Seminar
CalTitle1: AGR Seminar: Computing highly accurate frequentist confidence limits from discrete data
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Computing highly accurate frequentist confidence limits from discrete data
Professor Chris J. Lloyd (Melbourne Business School)
La Trobe University
For discrete data, frequentist 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.
Dr Andriy Olenko
If you would like to attend this seminar in our access grid room then please check to see if the grid is already booked at this time and send an email to firstname.lastname@example.org to let the CSOs know that you would like to attend.