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Application of Sampling Variance Smoothing Methods for Small Area Proportion Estimation

Author

Listed:
  • You Yong

    (Statistics Canada, Ottawa, K1A 0T6, Canada)

  • Hidiroglou Mike

    (Statistics Canada, Ottawa, K1A 0T6, Canada)

Abstract

Sampling variance smoothing is an important topic in small area estimation. In this article, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis and simulation study indicate that the proposed sampling variance smoothing methods perform very well for small area estimation.

Suggested Citation

  • You Yong & Hidiroglou Mike, 2023. "Application of Sampling Variance Smoothing Methods for Small Area Proportion Estimation," Journal of Official Statistics, Sciendo, vol. 39(4), pages 571-590, December.
    • Handle: RePEc:vrs:offsta:v:39:y:2023:i:4:p:571-590:n:5
    DOI: 10.2478/jos-2023-0026
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    References listed on IDEAS

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    1. Shonosuke Sugasawa & Hiromasa Tamae & Tatsuya Kubokawa, 2017. "Bayesian Estimators for Small Area Models Shrinking Both Means and Variances," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 150-167, March.
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