Open Access
May 2017 Probability Sampling Designs: Principles for Choice of Design and Balancing
Yves Tillé, Matthieu Wilhelm
Statist. Sci. 32(2): 176-189 (May 2017). DOI: 10.1214/16-STS606


The aim of this paper is twofold. First, three theoretical principles are formalized: randomization, overrepresentation and restriction. We develop these principles and give a rationale for their use in choosing the sampling design in a systematic way. In the model-assisted framework, knowledge of the population is formalized by modelling the population and the sampling design is chosen accordingly. We show how the principles of overrepresentation and of restriction naturally arise from the modelling of the population. The balanced sampling then appears as a consequence of the modelling. Second, a review of probability balanced sampling is presented through the model-assisted framework. For some basic models, balanced sampling can be shown to be an optimal sampling design. Emphasis is placed on new spatial sampling methods and their related models. An illustrative example shows the advantages of the different methods. Throughout the paper, various examples illustrate how the three principles can be applied in order to improve inference.


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Yves Tillé. Matthieu Wilhelm. "Probability Sampling Designs: Principles for Choice of Design and Balancing." Statist. Sci. 32 (2) 176 - 189, May 2017.


Published: May 2017
First available in Project Euclid: 11 May 2017

zbMATH: 1381.62032
MathSciNet: MR3648954
Digital Object Identifier: 10.1214/16-STS606

Keywords: Balanced sampling , cube method , design-based , Entropy , inference , model-based , pivotal method , spatial sampling

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.32 • No. 2 • May 2017
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