COMPLETE CLASS THEOREMS FOR UNBIASED ESTIMATION

Abstract

Let fx, theta) be a given density function continuous in theta, theta epsilon omega, where omega is a compact subset of the real line. Let D be the class of randomized unbiased estimators, delta(x), of theta. For squared error loss function, the cls of Bayes soltions s shown to be essenly complete relative to D. If omega is convex then every purely randomized delta(x) is inadmissible since by the Rao-Blackwell theorem epsilon delta (X)) has smaller risk. A theorem of STEIN CONCERNING LOCALLY BEST UNBIASED ESTIMATORS IS GENERALIZED TO PROVIDE CONDITIONS FOR UNIQUENES (and therefore adisibility) of Bayes solutions and functional equations for their determination. If the uniqueness condition is saisfied, then the bove lass is a complete class of admissible unbiased estimators. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1962
Accession Number
AD0287478

Entities

People

  • Eugene M. Laska

Organizations

  • New York University

Tags

DTIC Thesaurus Topics

  • Equations
  • Estimators
  • Mathematics

Fields of Study

  • Mathematics

Readers

  • Analytical Mechanics
  • Mathematical Modeling and Probability Theory.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms