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