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ReGenesees: an Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys Cover

ReGenesees: an Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys

Journal of Official Statistics
Volume 31 (2015): Issue 2 (June 2015)
By: Diego Zardetto  
Open Access
|Jun 2015

References

  1. Andersson, C. 2009. Using Auxiliary Information in the Calculation of Order Statistics and Estimated Totals in a Large Scale Production Environment. In Proceedings of the 57th Session of the International Statistical Institute (ISI). Durban, South Africa, 16-22 August 2009. Available at: http://isi.cbs.nl/iamamember/CD8-Durban2009/index.htm (accessed May 2015).
    Search in Google Scholar
  2. Andersson, C. and L. Nordberg. 1994. “A Method for Variance Estimation of Non-Linear Functions of Totals in Surveys - Theory and Software Implementation.” Journal of Official Statistics 10: 395-405.
    Search in Google Scholar
  3. Bellhouse, D.R. 1985. “Computing Methods for Variance Estimation in Complex Surveys.” Journal of Official Statistics 1: 323-329.
    Search in Google Scholar
  4. Caron, N. 1998. Le logiciel POULPE: aspects méthodologiques. In: INSEE: Actes des Journées de Méthodologie. Available at: http://jms.insee.fr/files/documents/1998/513_1-JMS1998_S3-1_CARON_P173-200.PDF (accessed May 2015).
    Search in Google Scholar
  5. Chambers, J.M. and T.J. Hastie. 1992. Statistical Models in S. London: Chapman & Hall.
    Search in Google Scholar
  6. Davidson, M. 2013. Scottish Population Surveys Centralised Weighting Project. Weighting project report of the Scottish Government. Available at: http://www.scotland.gov.uk/Topics/Statistics/About/Surveys/WeightingProjectReport (accessed August 2014).
    Search in Google Scholar
  7. Davies, P. and P. Smith. 1999. Model Quality Report in Business Statistics. Volume II: Comparison of Variance Estimation Software and Methods, EUROSTAT. Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/quality/documents/MODEL%20QUALITY%20REPORT%20VOL%202.pdf (accessed August 2014).
    Search in Google Scholar
  8. Deville, J.C. and C.-E. Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87: 376-382.10.1080/01621459.1992.10475217
    Search in Google Scholar
  9. Deville, J.C. 1999. “Variance Estimation for Complex Statistics and Estimators: Linearization and Residual Techniques.” Survey Methodology 25: 193-203.
    Search in Google Scholar
  10. Estevao, V., M.A. Hidiroglou, and C.-E. Särndal. 1995. “Methodological Principles for a Generalized Estimation System at Statistics Canada.” Journal of Official Statistics 11: 181-204.
    Search in Google Scholar
  11. EUROSTAT. 2002. Variance estimation methods in the European Union. Monographs of Official Statistics, Publications Office of the European Union, Luxembourg. Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/research_methodology/documents/MOS_20VARIANCE_ESTIMATION_202002.pdf (accessed August 2014).
    Search in Google Scholar
  12. EUROSTAT. 2013. Handbook on precision requirements and variance estimation for ESS households surveys. Methodologies & Working papers, Publications Office of the European Union, Luxembourg. Available at: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-13-029/EN/KS-RA-13-029-EN.PDF (accessed August 2014).
    Search in Google Scholar
  13. Falorsi, P.D. and S. Falorsi. 1997. “The Italian Generalised Package for Weighting Persons and Families: Some Experimental Results with Different Non-Response Models.” Statistics in Transition 3: 357-381.
    Search in Google Scholar
  14. Kalton, G. 1979. “Ultimate Cluster Sampling.” Journal of the Royal Statistical Society 142: 210-222.10.2307/2345081
    Search in Google Scholar
  15. Kott, P.S. 2001. “The Delete-A-Group Jackknife.” Journal of Official Statistics 17: 521-526.
    Search in Google Scholar
  16. Krewski, D. and J.N.K. Rao. 1981. “Inference from Stratified Sample: Properties of Linearization, Jackknife, and Balanced Repeated Replication Methods.” The Annals of Statistics 9: 1010-1019.10.1214/aos/1176345580
    Search in Google Scholar
  17. Lumley, T. 2004. “Analysis of Complex Survey Samples.” Journal of Statistical Software 9: 1-19.10.18637/jss.v009.i08
    Search in Google Scholar
  18. Lumley, T. 2010. Complex Surveys: A Guide to Analysis Using R. New York: John Wiley & Sons.10.1002/9780470580066
    Search in Google Scholar
  19. Miller, D. and P.S. Kott. 2011. “Using the DAG Jackknife to Measure the Variance of an Estimator in the Presence of Item Nonresponse.” In Proceedings of the JSM (July 30-August 4, 2011) Alexandria, VA: American Statistical Association, 1121-1129. Available at: http://nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2011/JSM-2011-Miller.pdf
    Search in Google Scholar
  20. Mohl, C. 2007. “The Continuing Evolution of Generalized Systems at Statistics Canada for Business Survey Processing.” In Proceedings of the Third International Conference on Establishment Surveys (ICESIII) (June 18-21, 2007), American Statistical Association, 758-768. Available at: http://www.amstat.org/meetings/ices/2007/proceedings/ICES2007-000135.PDF
    Search in Google Scholar
  21. Nieuwenbroek, N., R. Renssen, and L. Hofman. 2000. “Towards a Generalized Weighting System.” In Proceedings of the Second International Conference on Establishment Surveys (ICESII) (June 17-21, 2000), American Statistical Association, 667-676.
    Search in Google Scholar
  22. Available at: http://www.amstat.org/meetings/ices/2000/proceedings/S09.pdf
    Search in Google Scholar
  23. Ollila, P., Y. Berger, H.J. Boonstra, A. Davison, A. Laaksonen, K. Magg, R. Munnich, D. Ohly, S. Sardy, K. Sostra, and J. van den Brakel. 2004. “Evaluation of Software for Variance Estimation in Complex Surveys, DACSEIS project, Deliverables 4.1 and 4.2.” . Available at: https://www.uni-trier.de/fileadmin/fb4/projekte/SurveyStatisticsNet/Dacseis_Deliverables/DACSEIS-D4-1-4-2.pdf (accessed August 2014).
    Search in Google Scholar
  24. Osier, G. 2009. “Variance Estimation for Complex Indicators of Poverty and Inequality Using Linearization Techniques.” Survey Research Methods 3: 167-195.
    Search in Google Scholar
  25. R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org (accessed August 2014).
    Search in Google Scholar
  26. Rust, K. and G. Kalton. 1987. “Strategies for Collapsing Strata for Variance Estimation.” Journal of Official Statistics 3: 69-81.Särndal, C.-E., B. Swensson, and J. Wretman. 1989. “The Weighted Residual Technique for Estimating the Variance of the General Regression Estimator of the Finite Population Total.” Biometrika 76: 527-537. Doi: http://dx.doi.org/10.1093/biomet/76.3.527.10.1093/biomet/76.3.527
    Search in Google Scholar
  27. Särndal, C.-E. 2007. “The Calibration Approach in Survey Theory and Practice.” Survey Methodology 33: 99-119.
    Search in Google Scholar
  28. Sautory, O. 1993. La macro CALMAR: Redressement d’un Echantillon par Calage sur Marges. Document de travail de la Direction des Statistiques Démographiques et Sociales, no. F9310. Available at: http://www.insee.fr/fr/methodes/outils/calmar/doccalmar.pdf (accessed May 2015).
    Search in Google Scholar
  29. Scannapieco, M., D. Zardetto, and G. Barcaroli. 2007. La Calibrazione dei Dati con R: una Sperimentazione sull’Indagine Forze di Lavoro ed un Confronto con GENESEES/ SAS. Collana Contributi, 4, Istat, Italy. Available at: http://www3.istat.it/dati/pubbsci/contributi/Contributi/contr_2007/2007_4.pdf (accessed August 2014).
    Search in Google Scholar
  30. Scottish Government. 2013a. Scottish Household Survey - Methodology and Fieldwork Outcomes 2012. Available at: http://www.scotland.gov.uk/Resource/0044/00443332.pdf (accessed August 2014).
    Search in Google Scholar
  31. Scottish Government. 2013b. Scottish Health Survey 2012 - Volume 2 Technical Report. Available at: http://www.scotland.gov.uk/Resource/0043/00434643.pdf (accessed August 2014).
    Search in Google Scholar
  32. Scottish Government. 2014. Scottish Crime and Justice Survey 2012/13 - Technical Report. Available at: http://www.scotland.gov.uk/Resource/0044/00445791.pdf (accessed August 2014).
    Search in Google Scholar
  33. UNECE. 2013a. Generic Statistical Information Model (GSIM), version 1.1. Available at: http://www1.unece.org/stat/platform/pages/viewpage.action?pageId¼59703371 (accessed August 2014).
    Search in Google Scholar
  34. UNECE. 2013b. Common Statistical Production Architecture (CSPA), version 1.0. Available at: http://www1.unece.org/stat/platform/display/CSPA/CSPAþv1.0 (accessed August 2014).
    Search in Google Scholar
  35. UNECE. 2013c. Generic Statistical Business Process Model (GSBPM), version 5.0. Available at: http://www1.unece.org/stat/platform/display/metis/TheþGenericþStatisticalþBusinessþProcessþModel (accessed August 2014).
    Search in Google Scholar
  36. Vanderhoeft, C. 2001. Generalised Calibration at Statistics Belgium. SPSS Module g-CALIB-S and Current Practices. Statistics Belgium Working Paper no. 3. Available at: http://statbel.fgov.be/nl/binaries/paper03%5B1%5D_tcm325-35412.pdf (accessed May 2015).
    Search in Google Scholar
  37. Wilkinson, G.N. and C.E. Rogers. 1973. Symbolic Description of Factorial Models for Analysis of Variance. Journal of the Royal Statistical Society. Series C (Applied Statistics) 22: 392-399.10.2307/2346786
    Search in Google Scholar
  38. Wolter, K.M. 2007. Introduction to Variance Estimation, Second Edition. New York: Springer.
    Search in Google Scholar
  39. Woodruff, R.S. 1952. Confidence Intervals for Medians and Other Position Measures. Journal of the American Statistical Association 47: 635646. Doi: http://dx.doi.org/10.1080/01621459.1952.1048344310.1080/01621459.1952.10483443
    Search in Google Scholar
  40. Woodruff, R.S. 1971. “A Simple Method for Approximating the Variance of a Complicated Estimate.” Journal of the American Statistical Association 66: 411-414. Doi: http://dx.doi.org/10.1080/01621459.1971.10482279.10.1080/01621459.1971.10482279
    Search in Google Scholar
  41. Zardetto, D. 2012. EVER: Estimation of Variance by Efficient Replication. R package version 1.2, Istat, Italy. Available at: http://cran.r-project.org/web/packages/EVER/index.html (accessed August 2014).
    Search in Google Scholar
  42. Zardetto, D. 2014. ReGenesees: R Evolved Generalized Software for Sampling Estimates and Errors in Surveys. R package version 1.6, Istat, Italy. Available at: https://joinup.ec.europa.eu/software/regenesees/description (accessed August 2014).
    Search in Google Scholar
  43. Zardetto, D. and R. Cianchetta. 2014. ReGenesees.GUI: a TclTk Interface for the ReGenesees Package. R package version 1.6, Istat, Italy. Available at: https://joinup.ec.europa.eu/software/regenesees/description (accessed August 2014).
    Search in Google Scholar
DOI: https://doi.org/10.1515/jos-2015-0013 | Journal eISSN: 2001-7367
Journal RSS Feed
Language: English
Page range: 177 - 203
Submitted on: Jul 1, 2013
|
Accepted on: Aug 1, 2014
|
Published on: Jun 27, 2015
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Complex estimators,
variance estimation,
automated linearization,
symbolic computation
Related subjects:
Mathematics,
Probability and statistics

© 2015 Diego Zardetto, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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