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.


  • Workflow Status: Published
  • Created By: Barbara Christopher
  • Created: 10/08/2010
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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