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Minimax estimation method for the optimum decomposition of a sample space based on prior information

  • Kazuo Noda
  • Yasushi Taga
Article

Summary

Existences and expressions of minimax estimators based on prior information for the optimum decomposition of a given sample space are studied in a unifying way such that answers can be easily obtained by applying the theorems given in this paper to various statistical problems—optimum selection regions, tolerance regions, prediction regions, optimum stratifications and so on.

Further, ε-approximations to those estimators are given by choosing a finite family of probability measures from the infinite family of them under consideration in such a way that the former may be considered to be a sufficiently good approximation to the latter in the sense of risk function.

Besides, those estimators are proved to be consistent in the sense that the risk decreases to zero as the amount of prior information increases infinitely.

Keywords

Probability Measure Prior Information Risk Function Decision Function Weak Topology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Institute of Statistical Mathematics 1971

Authors and Affiliations

  • Kazuo Noda
  • Yasushi Taga

There are no affiliations available

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