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Efficient balanced sampling: The cube method

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  • Jean-Claude Deville
  • Yves Tille

Abstract

A balanced sampling design is defined by the property that the Horvitz--Thompson estimators of the population totals of a set of auxiliary variables equal the known totals of these variables. Therefore the variances of estimators of totals of all the variables of interest are reduced, depending on the correlations of these variables with the controlled variables. In this paper, we develop a general method, called the cube method, for selecting approximately balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variables. Copyright 2004, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/biomet/91.4.893
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Bibliographic Info

Article provided by Biometrika Trust in its journal Biometrika.

Volume (Year): 91 (2004)
Issue (Month): 4 (December)
Pages: 893-912

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Handle: RePEc:oup:biomet:v:91:y:2004:i:4:p:893-912

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Cited by:
  1. Tillé, Yves & Favre, Anne-Catherine, 2005. "Optimal allocation in balanced sampling," Statistics & Probability Letters, Elsevier, vol. 74(1), pages 31-37, August.
  2. Hasler, Caren & Tillé, Yves, 2014. "Fast balanced sampling for highly stratified population," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 81-94.

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