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Abstract Details

Activity Number: 497
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #304621
Title: Inferences from Prior-Based Loss Functions
Author(s): Gun Ho Jang*+ and Michael Evans
Companies: University of Pennsylvania and University of Toronto
Address: 210 Blockley Hall, 423 Guardian Dr., Philadelphia, PA, 19104, United States
Keywords: loss functions ; relatve surprse ; lowest posterior risk region ; Bayesian unbiasedness

Inferences that arise from loss functions determined by the prior are considered and it is shown that these lead to limiting Bayes rules that are closely connected with likelihood. The procedures obtained via these loss functions are invariant under reparameterizations and are Bayesian unbiased or limits of Bayesian unbiased inferences. These inferences serve as well-supported alternatives to MAP-based inferences.

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