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CIDID Seminar: Bradley Efron

  • University of Washington, School of Public Health Room T-639, Health Sciences Bldg, 1959 Northeast Pacific Street Seattle, WA, 98195 United States (map)

NOTE: This seminar is co-sponsored with the University of Washington Department of Biostatistics.

Confidence densities, uninformative priors, and the bootstrap

Seminar Speaker: Bradley Efron, PhD

Professor of Statistics and Biomedical Data Science
Stanford University


There have been a series of conferences around the world under the label "BFF", standing for Bayesian, Frequentist, and Fiducial. I will give a version of the keynote talk at the most recent one. A general problem of BFF interest goes as follows: A family of densities with vector parameter "mu" has yielded data "X", from which the statistician wishes to infera real-valued parameter theta = t(mu). For example X might be multi-variate normal, X~N(m,V), and theta the trace of V. A statistical holy grail task is to find a convincing posterior density of theta given X, when there is no prior information on the distribution of mu. A suite of more or less related answers have been proposed: uninformative priors, matching priors, fiducial methods, and confidence densities (the last being derivatives of confidence distributions.) This talk reviews the various theories, connecting them to bootstrap methods for their implementation.

Download flyer: [pdf]

Earlier Event: September 28
CIDID Seminar: Louise Moncla
Later Event: January 13
CIDID Seminar: Nathan Grubaugh