Survey Methodology
Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference

by Roderick J. LittleNote 1

  • Release date: December 15, 2022
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Abstract

Conceptual arguments and examples are presented suggesting that the Bayesian approach to survey inference can address the many and varied challenges of survey analysis. Bayesian models that incorporate features of the complex design can yield inferences that are relevant for the specific data set obtained, but also have good repeated-sampling properties. Examples focus on the role of auxiliary variables and sampling weights, and methods for handling nonresponse. The article offers ten top reasons for favoring the Bayesian approach to survey inference.

Key Words: Calibrated Bayes inference; Design-based inference; Penalized splines; Post-stratification; Probability proportional to size sampling; Proxy pattern-mixture models; Response propensity; Super-population models; Survey weighting.

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How to cite

Little, R.J. (2022). Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 2. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2022002/article/00001-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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Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada as represented by the Minister of Industry, 2022

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Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Notation, and a seminal paper
Section 3. Design-based versus model-based inference
Section 4. Examples
Section 5. Conclusion: Ten reasons to be Bayesian for survey inference
Acknowledgements
References

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