Survey Methodology
Robust Bayesian small area estimation

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by Malay Ghosh, Jiyoun Myung and Fernando A.S. MouraNote 1

  • Release date: June 21, 2018
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Abstract

Small area models handling area level data typically assume normality of random effects. This assumption does not always work. The present paper introduces a new small area model with t random effects. Along with this, this paper also considers joint modeling of small area means and variances. The present approach is shown to perform better than other methods.

Key Words:      Random effects model; Student’s t-distribution; Non-subjective priors; MCMC; Gibbs sampling; Metropolis-Hastings algorithm.

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How to cite

Ghosh, M., Myung, J. and Moura, F.A.S. (2018). Robust Bayesian small area estimation. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 44, No. 1. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2018001/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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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

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. The model
Section 3. Application
Section 4. Final remarks
Acknowledgements
Appendix A
Appendix B
References

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