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Small Area Estimation in the German Census 2011

Author

Listed:
  • Münnich Ralf

    (University of Trier, ; Trier, ; German)

  • Burgard Jan Pablo

    (University of Trier, ; Trier, ; German)

  • Gabler Siegfried

    (GESIS Mannheim, ; Trier, ; German)

  • Ganninger Matthias

    (Roche Diagnostics, ; Trier, ; German)

  • Kolb Jan-Philipp

    (GESIS Mannheim, ; Trier, ; German)

Abstract

In 2011, Germany conducted the first census after the reunification. In contrast to a classical census, a register-assisted census was implemented using population register data and an additional sample. This paper provides an overview of how the sampling design recommendations were set up in order to fulfil legal requirements and to guarantee an optimal but still flexible source of information. The aim was to develop a design that fosters an accurate estimation of the main objective of the census, the total population counts. Further, the design should also adequately support the application of small area estimation methods. Some empirical results are given to provide an assessment of selected methods. The research was conducted within the German Census Sampling and Estimation research project, financially supported by the German Federal Statistical Office.

Suggested Citation

  • Münnich Ralf & Burgard Jan Pablo & Gabler Siegfried & Ganninger Matthias & Kolb Jan-Philipp, 2016. "Small Area Estimation in the German Census 2011," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 25-40, March.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:1:p:25-40:n:4
    DOI: 10.21307/stattrans-2016-004
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    References listed on IDEAS

    as
    1. Gonzalez-Manteiga, W. & Lombardia, M.J. & Molina, I. & Morales, D. & Santamaria, L., 2007. "Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2720-2733, February.
    2. Siegfried Gabler & Matthias Ganninger & Ralf Münnich, 2012. "Optimal allocation of the sample size to strata under box constraints," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(2), pages 151-161, February.
    3. Friedrich, Ulf & Münnich, Ralf & de Vries, Sven & Wagner, Matthias, 2015. "Fast integer-valued algorithms for optimal allocations under constraints in stratified sampling," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 1-12.
    4. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
    5. Ralf Münnich & Ekkehard Sachs & Matthias Wagner, 2012. "Numerical solution of optimal allocation problems in stratified sampling under box constraints," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 435-450, July.
    Full references (including those not matched with items on IDEAS)

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