Article
Uniformly Hyper-Efficient Bayes Inference in a Class of Nonregular Problems
The American Statistician
Document Type
Article
Disciplines
Publication Version
Accepted Manuscript
Publication Date
1-1-2009
DOI
10.1198/tast.2009.08170
Abstract
We present a tractable class of nonregular continuous statistical models where 1) likelihoods have multiple singularities and ordinary maximum likelihood is intrinsically unavailable, but 2) Bayes procedures achieve convergence rates better than n−1 across the whole parameter space. In fact, for every p>1, there is a member of the class for which the posterior distribution is consistent at rate n−puniformly in the parameter.
Copyright Owner
American Statistical Association
Copyright Date
2009
Language
en
File Format
application/pdf
Citation Information
Danial J. Nordman, Stephen B. Vardeman and Melissa Ann Bingham. "Uniformly Hyper-Efficient Bayes Inference in a Class of Nonregular Problems" The American Statistician Vol. 63 Iss. 2 (2009) p. 234 - 238 Available at: http://works.bepress.com/stephen_vardeman/28/
This is an Accepted Manuscript of an article published by Taylor & Francis in The American Statistician in 2009, available online: http://www.tandfonline.com/10.1198/tast.2009.08170.