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2016 | 17 | nr 1 | 25--40
Tytuł artykułu

Small Area Estimation in the German Census 2011

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. (original abstract)
Rocznik
Tom
17
Numer
Strony
25--40
Opis fizyczny
Twórcy
  • University of Trier
  • University of Trier
  • GESIS Mannheim
  • Roche Diagnostics
  • GESIS Mannheim
Bibliografia
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  • BECHTOLD, S., (2013). The new register-based census of Germany - a multiple source mixed mode approach. In: Presentation on the 59th World Statistics Congress (WSC), Hong Kong, August 2013, URL http://www.statistics.gov.hk/wsc/IPS027-P2-S.pdf.
  • FAY, R. E., HERRIOT, R. A., (1979). Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, Vol. 74, No. 366, pp. 269-277.
  • FRIEDRICH, U., MÜNNICH, R., DE VRIES, S., WAGNER, M., (2015). Fast integer- valued algorithms for optimal allocations under constraints in stratified sampling. Resubmitted.
  • GABLER, S., GANNINGER, M., MÜNNICH, R., (2012). Optimal allocation of the sample size to strata under box constraints. Metrika 75(2), pp. 151-161.
  • GELMAN, A., (2007). Struggles with survey weighting and regression modeling. Statistical Science, pp. 153-164.
  • 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. Comput Stat Data Anal 51:2720- 2733, DOI 10. 1016/j.csda.2006.01.012.
  • GUIBLIN, P., LONGFORD, N., HIGGINS, N., (2004). Standard estimators for small areas: Sas programs and documentation. Tech. rep., EURAREA - IST-2000-26290.
  • JIANG, J., LAHIRI, P., (2001). Empirical best prediction for small area inference with binary data. Annals of the Institute of Statistical Mathematics 53, pp. 217243, dOI 10.1023/A: 1012410420337.
  • KLEBER, B., MALDONADO, A., SCHEUREGGER, D., ZIPRIK, K., (2009). Aufbau des anschriften- und gebäuderegisters für den zensus 2011. Wirtschaft und Statistik 7, pp. 629-640.
  • KOLB, J. P., (2013). Methoden zur erzeugung synthetischer simulationsgesamtheiten. PhD thesis, Universität Trier.
  • MÜNNICH, R., MAGG, K., SOSTRA, K., SCHMIDT, K., WIEGERT, R., (2004). Workpackage 10: Variance estimation for small area estimates: Deliverables 10.1 and 10.2. URL http://www dacseis de-IST-2000-26057-DACSEIS Reports.
  • MÜNNICH, R., BURGARD, J. P., VOGT, M., (2009). Small area estimation for population counts in the German Census 2011. In: Proceedings of the Joint Statistical Meeting of the American Statistical Association. Washington.
  • MÜNNICH, R., BURGARD, P., VOGT, M., (2009). Small area estimation for population counts in the German Census 2011. In: Section on Survey Research Methods JSM 2009.
  • MÜNNICH, R., GABLER, S., GANNINGER, M., BURGARD, J. P., KOLB, J. P., (2012a). Stichprobenoptimierung und Schätzung im Zensus 2011. Statistisches Bundesamt.
  • MÜNNICH, R., SACHS, E. W., WAGNER, M., (2012b). Numerical solution of optimal allocation problems in stratified sampling under box constraints. AStA Advances in Statistical Analysis 96 (3), pp. 435-450.
  • MÜNNICH, R., WAGNER, M., SACHS, E. W., (2012c). Calibration benchmarking for small area estimates: An application to the German Census 2011. Symposium on the Anal- ysis of Survey Data and Small Area Estimation in Honour of the 75th Birthday of J. N. K Rao.
  • RAO, J. N. K., (2003). Small Area Estimation. Wiley series in survey methodology, John Wiley and Sons, New York.
  • THE EURAREA CONSORTIUM, (2004). Project reference volume vol. 2: Explanatory appendices. Tech. rep., EURAREA - IST-2000-26290.
  • TILLE, Y., (2011) Sampling algorithms. Springer.
  • WAGNER, M., (2013). Numerical optimization in survey statistics. PhD thesis, Univer- sität Trier, Universitätsring 15, 54296 Trier.
  • YOU, Y., RAO, J. N. K., (2002). A pseudo-empirical best linear unbiased prediction approach to small area estimation using survey weights. The Canadian Journal of Statistics / La Revue Canadienne de Statistique 30(3), pp.431-439.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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