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The Problem of Dimensionality in Stratified Sampling

Author

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  • Russell C. H. Cheng

    (Department of Mathematics, UWIST, Cardiff, United Kingdom)

  • Teresa Davenport

    (Department of Mathematics, UWIST, Cardiff, United Kingdom)

Abstract

Stratified sampling is perhaps the most natural of the variance reduction techniques. However its use is often frustrated by the high dimensionality of the sample space. This paper investigates the difficulty and suggests a basic sampling scheme for use in such problems. The accuracy of estimators when this method of sampling is used is examined in detail. A way of implementing the scheme in practice is suggested which makes use of shadow response variables (variables which have similar properties to control variables). This reduces the dimensionality of the sample space to a tractable size. Two detailed examples are given for which a 10% to 90% reduction in variance is obtained compared with crude Monte Carlo.

Suggested Citation

  • Russell C. H. Cheng & Teresa Davenport, 1989. "The Problem of Dimensionality in Stratified Sampling," Management Science, INFORMS, vol. 35(11), pages 1278-1296, November.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:11:p:1278-1296
    DOI: 10.1287/mnsc.35.11.1278
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    Cited by:

    1. Fakhereddine, Rana & Haddad, Rami El & Lécot, Christian & Maalouf, Joseph El, 2017. "Stratified Monte Carlo simulation of Markov chains," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 135(C), pages 51-62.

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