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Multivariate Stratified Sampling by Optimization

Author

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  • John M. Mulvey

    (Princeton University)

Abstract

An important, recurring problem in statistics involves the determination of strata boundaries for use in stratified sampling. This paper 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.

Suggested Citation

  • John M. Mulvey, 1983. "Multivariate Stratified Sampling by Optimization," Management Science, INFORMS, vol. 29(6), pages 715-724, June.
    • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:6:p:715-724
    DOI: 10.1287/mnsc.29.6.715
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    Cited by:

    1. Jirachai Buddhakulsomsiri & Parthana Parthanadee, 2008. "Stratified random sampling for estimating billing accuracy in health care systems," Health Care Management Science, Springer, vol. 11(1), pages 41-54, March.
    2. Bretthauer, Kurt M. & Ross, Anthony & Shetty, Bala, 1999. "Nonlinear integer programming for optimal allocation in stratified sampling," European Journal of Operational Research, Elsevier, vol. 116(3), pages 667-680, August.

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