Bayes and Equivariant Estimators of the Variance of a Finite Population

Abstract

The problem of estimating the variance of a finite population is studied in a Bayesian framework. On the basis of the modern theoretical approach to sampling from finite populations and the special structure of the likelihood functions Bayes estimators of the population variance are derived. The structure of equivariant estimators is analyzed and Bayes equivariant estimators in the strict and the generalized sense are derived. Posterior and prior efficiency of the estimators is discussed.

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Document Details

Document Type
Technical Report
Publication Date
Feb 21, 1979
Accession Number
ADA065323

Entities

People

  • Herbert Solomon
  • S. Zacks

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Contracts
  • Cooperation
  • Efficiency
  • Estimators
  • Sampling

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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