Adaptive FAB confidence intervals with constant coverage
Confidence intervals for the means of multiple normal populations are often based on a hierarchical normal model. While commonly used interval procedures based on such a model have the nominal coverage rate on average across a population of groups, their actual coverage rate for a given group will be above or below the nominal rate, depending on the value of the group mean.
In this talk I present confidence interval procedures that have constant frequentist coverage rates and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard t-intervals on average across groups.
These intervals are obtained by inverting Bayes-optimal frequentist tests, and so are “frequentist, assisted by Bayes” (FAB). I present some asymptotic optimality results and some extensions to other scenarios, such as linear regression and tensor analysis.
16 June 2017, 15:30 (Friday, 8th week, Trinity 2017)
24-29 St Giles', 24-29 St Giles' OX1 3LB
Large Lecture Theatre, Department of Statistics
Speaker to be announced
Department of Statistics
Professor Arnaud Doucet (University of Oxford)
Organiser contact email address:
Distinguished Speaker Seminar
Members of the University only