Mathematical Models for Statistical Decision Theory

Abstract

Three methods of defining optimality of statistical decision rules are introduced. The first uses ideas of approximation theory by defining the optimal decision as that element of the risk set which best approximates an ideal rule. The second optimality principle defines optimality in terms of minimizing functionals. The third method is the axiomatization of optimality in statistical decision theory.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1971
Accession Number
AD0737250

Entities

People

  • Bernard Harris

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Bayesian Inference
  • Convex Sets
  • Data Science
  • Decision Theory
  • Distribution Functions
  • Information Science
  • Mathematical Models
  • Mathematics
  • Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Decision Theory
  • Statistical Inference
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.