Advertisement

TEST

, Volume 28, Issue 4, pp 1077–1081 | Cite as

Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem

  • Maria del Mar RuedaEmail author
Discussion
  • 27 Downloads

Introduction

It is a pleasure to comment on this interesting article by Devaud and Tillé, whose paper gives us the opportunity to reflect on the important impact produced by this method of sampling parameter estimation. Since the seminal work by Deville and Särndal, calibration has been one of the most useful tools available with which to incorporate auxiliary information in survey sampling. This technique ensures that the estimates obtained are coherent with those already in the public domain, while simultaneously reducing non-coverage, non-response and selection biases (Arcos et al. 2014). Although other important estimation methods that also use auxiliary information have been proposed (e.g. the empirical likelihood method; Chen and Qin 1993 or that of model-based estimators; Valliant et al. 2000), in practice, the vast majority of national statistical agencies use calibration as a reweighting technique and have developed software to compute calibrated weights in accordance with...

Notes

Acknowledgements

This study was partially supported by Ministerio de Economía y Competitividad (Grant MTM2015-63609-R).

References

  1. Arcos A, Contreras JM, Rueda M (2014) A novel calibration estimator in social surveys. Sociol Methods Res 43(3):465–489MathSciNetCrossRefGoogle Scholar
  2. Arcos A, Martínez S, Rueda M, Martínez H (2017) Distribution function estimates from dual frame context. J Comput Appl Math 318:242–252MathSciNetCrossRefGoogle Scholar
  3. Bethlehem J (2010) Selection bias in web surveys. Int Stat Rev 78(2):161–188CrossRefGoogle Scholar
  4. Breidt J, Opsomer J (2017) Model-assisted survey estimation with modern prediction. Stat Sci 32(2):190–205MathSciNetCrossRefGoogle Scholar
  5. Buelens B, Burger J, van den Brakel JA (2018) Comparing inference methods for non-probability samples. Int Stat Rev 86(2):322–343MathSciNetCrossRefGoogle Scholar
  6. Cardot H, Josserand J (2011) Horvitz–Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling. Biometrika 98:107–118MathSciNetCrossRefGoogle Scholar
  7. Chen J, Qin J (1993) Empirical likelihood estimation for finite populations and the effective usage of auxiliary information. Biometrika 80:107–116MathSciNetCrossRefGoogle Scholar
  8. Ferri R, Rueda M (2018) Efficiency of propensity score adjustment and calibration on the estimation from non-probabilistic online surveys. SORT 42(2):159–182MathSciNetzbMATHGoogle Scholar
  9. Ferri R, Castro L, Rueda M (2019) Superpopulation models using machine learning methods for estimation in online surveys. In: Proceeding of mathematical and computational modelling, approximation and simulation. MACMAS 2019Google Scholar
  10. Gamboa S, Gallón F, Loubes JM (2015) Functional calibration estimation by the maximum entropy on the mean principle. Statistics 49(5):989–1004MathSciNetCrossRefGoogle Scholar
  11. Harms T, Duchesne P (2006) On calibration estimation for quantiles. Surv Methodol 32:37–52Google Scholar
  12. Kim JK, Park M (2010) Calibration estimation in survey sampling. Int Stat Rev 78:21–39CrossRefGoogle Scholar
  13. Lee S, Valliant R (2009) Estimation for volunteer panel web surveys using propensity score adjustment and calibration adjustment. Sociol Methods Res 37(3):319–343MathSciNetCrossRefGoogle Scholar
  14. Martínez S, Rueda M, Arcos A, Martínez H (2010) Optimum calibration points estimating distribution functions. J Comput Appl Math 233(9):2265–2277MathSciNetCrossRefGoogle Scholar
  15. Martínez S, Rueda M, Martínez H, Arcos A (2015) Determining P optimum calibration points to construct calibration estimators of the distribution function. J Comput Appl Math 275:281–293MathSciNetCrossRefGoogle Scholar
  16. Martínez S, Rueda M, Martínez H, Arcos A (2017) Optimal dimension and optimal auxiliary vector to construct calibration estimators of the distribution function. J Comput Appl Math 318:444–459MathSciNetCrossRefGoogle Scholar
  17. Mayor Gallego JA, Moreno Rebollo JL, Jiménez Gamero MD (2019) Estimation of the finite population distribution function using a global penalized calibration method. AStA Adv Stat Anal 103:1–35MathSciNetCrossRefGoogle Scholar
  18. Montanari GE, Ranalli MG (2005) Nonparametric model calibration estimation in survey sampling. J Am Stat Assoc 100:1429–1442MathSciNetCrossRefGoogle Scholar
  19. Montanari GE, Ranalli MG (2009) Multiple and ridge model calibration. In: Proceedings of workshop on calibration and estimation in surveys. Statistics CanadaGoogle Scholar
  20. Park M, Fuller WA (2009) The mixed model for survey regression estimation. J Stat Plan Inference 139:1320–1331MathSciNetCrossRefGoogle Scholar
  21. Plikusas A (2006) Non-linear calibration. In: Proceedings, workshop on survey sampling. Central Statistical Bureau of Latvia, Ventspils, Latvia, RigaGoogle Scholar
  22. Rueda M, Martínez S, Martínez H, Arcos A (2007) Estimation of the distribution function with calibration methods. J Stat Plan Inference 137(2):435–448MathSciNetCrossRefGoogle Scholar
  23. Rueda M, Sánchez-Borrego I, Arcos A, Martínez S (2010) Model-calibration estimation of the distribution function using nonparametric regression. Metrika 71:33–44MathSciNetCrossRefGoogle Scholar
  24. Singh S (2001) Generalized calibration approach for estimating variance in survey sampling. Ann Inst Stat Math 53:404–417MathSciNetCrossRefGoogle Scholar
  25. Valliant R (2019) Comparing alternatives for estimation from nonprobability samples. Technical report.  https://doi.org/10.13140/RG.2.2.14618.90566
  26. Valliant R, Dever JA (2011) Estimating propensity adjustments for volunteer web surveys. Sociol Methods Res 40(1):105–137MathSciNetCrossRefGoogle Scholar
  27. Valliant R, Dorfman AH, Royall RM (2000) Finite population sampling and inference. A prediction approach. Wiley, New YorkzbMATHGoogle Scholar
  28. Wu C, Sitter RR (2001) A model-calibration approach to using complete auxiliary information from survey data. J Am Stat Assoc 96(453):185–193MathSciNetCrossRefGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Department of Statistics and O.R.University of GranadaGranadaSpain

Personalised recommendations