BAYESIAN DESIGN OF SINGLE, DOUBLE, AND SEQUENTIAL STRATIFIED SAMPLING FOR ESTIMATING PROPORTION IN FINITE POPULATIONS.

Abstract

The problem of optimal allocation in stratified sampling to estimate a vector of proportions is studied from the Bayesian point of view. A linear function of the proportions is analyzed to make the problem more manageable. In particular, an investigation of double and sequential Bayes stratified sampling is performed. The methodology is outlined and two special cases are worked out: the cases of binomial and of uniform prior distributions of the number of elements in the strata having a certain attribute. The Bayesian efficiency of inverse sequential stratified sampling is studied in relation to the Bayesian stratified simple random sampling. (Author)

Document Details

Document Type
Technical Report
Publication Date
Sep 22, 1967
Accession Number
AD0660045

Entities

People

  • S. Zacks

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Binomials
  • Collecting Methods
  • Sampling
  • Statistical Sampling

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Naval Personnel Management
  • Neural Network Machine Learning.

Technology Areas

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