OPTIMAL BAYESIAN STRATIFIED SAMPLING BY NONLINEAR PROGRAMMING.

Abstract

The optimum allocation for stratified sampling is studied under the Bayesian assumption that prior information concerning the unknown stratum means is available. The prior information is assumed to be expressed in the form of a multivariate normal prior distribution for the unknown stratum means. There are r sub i variates of interest in the i th stratum, so that each stratum mean is a vector. The cost of sampling is the sum of the costs of sampling the various strata, and the cost of sampling the i th stratum is taken to be a constant c sub i times the size of the sample taken from that stratum. Not all strata need be sampled. Several allocation problems are formulated and shown to be solvable by the standard algorithms of nonlinear programming. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1966
Accession Number
AD0637593

Entities

People

  • Richard M. Soland

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Evolutionary Algorithms
  • Heuristic Methods
  • Mathematics
  • Nonlinear Programming
  • Sampling
  • Standards

Fields of Study

  • Mathematics

Readers

  • Groundwater Contamination Remediation.
  • Statistical inference.

Technology Areas

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