MULTIVARIATE STRATIFIED SAMPLING BY OPTIMIZATION.

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Abstract

This study describes a practical method for stratifying a population of observations based on optimal cluster analysis. The goal of stratification is constructing a partition such that observations within a stratum are homogeneous as defined by within-cluster variances for attributes that are deemed important, while observations between strata are heterogeneous. The problem is defined as a deterministic optimization model with integer variables and is solved by means of a subgradient method. Computational tests with several examples show that the within-strata variances and thus the accompanying standard errors can be substantially reduced. Since the proposed model strives to minimize standard error, it is applicable to situations where a precise sample is essential, for example, microeconomic simulation studies.

Original languageEnglish (US)
Pages (from-to)715-724
Number of pages10
JournalManagement Science
Volume29
Issue number6
DOIs
StatePublished - Jan 1 1983

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research

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