The Royal Statistical Society sent me a letter announcing their latest Journal webinar next Wednesday 21 October:
…RSS Journal webinar on 21st October featuring Bradley Efron, Andrew Gelman and Peter Diggle. They will be in discussion about Bradley Efron’s recently published paper titled ‘Frequentist accuracy of Bayesian estimates’. The paper was published in June in the Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol 77 (3), 617-646. It is free to access from October 7th to November 4th.
Webinar start time: 8 am in California (PDT); 11 am in New York (EDT); 4pm (UK time).
During the webinar, Bradley Efron will present his paper for about 30 minutes followed by a Q&A session with the audience. Andrew Gelman is joining us as discussant and the event will be chaired by our President, Peter Diggle. Participation in the Q&A session by anyone who dials in is warmly welcomed and actively encouraged.Participants can ask the author a question over the phone or simply issue a message using the web based teleconference system. Questions can be emailed in advance and further information can be requested from email@example.com.
More details about this journal webinar and how to join can be found in StatsLife and on the RSS website. RSS Journal webinars are sponsored by Quintiles.
We’d be delighted if you were able to join us on the 21st and very grateful if you could let your colleagues and students know about the event.
I will definitely be tuning in!
Another amazing development of statistical methodology by Bradley Efron.
I find it fascinating that there is very little discussion of this methodology – I think people are still picking up their jaws from the floor.
There’s a bit of confused hand-waving by some religious Bayesians on this old discussion thread
and some in-depth Bayesian reflection offered at
“For symmetry, we should do a ‘Bayesian properties of Frequentist answers’.
How well does a Confidence Interval represent the range of possibilities compatible with the evidence? Well since some 95% CI’s contain values all of which are provably impossible from the same assumptions used to derive the CI, they suck.”
I’m going to read and re-read Efron’s paper and computer code and work towards incorporating his beautiful insight into my analyses, where appropriate.