Survey Methodology
Optimizing a mixed allocation

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by Antoine Rebecq and Thomas Merly-AlpaNote 1

  • Release date: December 20, 2018
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Abstract

This article proposes a criterion for calculating the trade-off in so-called “mixed” allocations, which combine two classic allocations in sampling theory. In INSEE (National Institute of Statistics and Economic Studies) business surveys, it is common to use the arithmetic mean of a proportional allocation and a Neyman allocation (corresponding to a trade-off of 0.5). It is possible to obtain a trade-off value resulting in better properties for the estimators. This value belongs to a region that is obtained by solving an optimization program. Different methods for calculating the trade-off will be presented. An application for business surveys is presented, as well as a comparison with other usual trade-off allocations.

Key Words:      Sampling; calculation of allocation; optimization; dispersion of weights; Neyman allocation.

Table of contents

How to cite

Rebecq, A., and Merly-Alpa, T. (2018). Optimizing a mixed allocation. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 44, No. 2. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2018002/article/54959-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Copyright

Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2018

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

Ottawa

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Optimization program
Section 3. Choosing λ
Section 4. Practical application
Section 5. Conclusion
Appendix A
Appendix B
References

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