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STATISTICS SEMINAR: Gamma-Minimax Inference Without Tears
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In the early 1950's Herbert Robbins (1915-2001)
introduced the
Gamma-Minimax or Bayes-Minimax Paradigm,
as a compromise between Minimax and Bayes
Paradigms. Gamma-Minimax actions rely on
Min-Max type theorems and are often hard or
impossible to find.
In this talk we overview linear approximations
to Gamma-minimax actions and demonstrate
that in many decision theoretic problems
linear approximations are not substantially affecting
the risk of the decision maker.
The talk is a mixture of educational and research overviews,
and does not assume prior exposure to minimaxity and
Gamma-minimaxity.
Status
- Workflow Status: Published
- Created By: Barbara Christopher
- Created: 10/08/2010
- Modified By: Fletcher Moore
- Modified: 10/07/2016
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