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Rejoinder on: Deville and Särndal’s calibration: revisiting a 25-year-old successful optimization problem

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References

  • Boistard H, Lopuhaä HP, Ruiz-Gazen A (2017) Functional central limit theorems for single-stage sampling designs. Ann Stat 45(4):1728–1758

    Article  MathSciNet  Google Scholar 

  • Cardot H, Josserand E (2011) Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling. Biometrika 98(1):107–118

    Article  MathSciNet  Google Scholar 

  • Chang T, Kott PS (2008) Using calibration weighting to adjust for nonresponse under a plausible model. Biometrika 95:555–571

    Article  MathSciNet  Google Scholar 

  • Cressie N, Read TRC (1984) Multinomial goodness-of-fit tests. J R Stat Soc Ser B (Methodol) 46(3):440–464

    MathSciNet  MATH  Google Scholar 

  • Deville JC (1998) La correction de la non-réponse par calage ou par échantillonnage équilibré. Technical report In see, Paris, recueil de la Section des méthodes d’enquête

  • Deville JC (2000) Generalized calibration and application to weighting for non-response. Compstat—proceedings in computational statistics: 14th symposium held in Utrecht. Springer, New York, pp 65–76

    Chapter  Google Scholar 

  • Deville JC (2002) La correction de la nonréponse par calage généralisé. Actes des Journées de Méthodologie Statistique. Insee-Méthodes, Paris, pp 3–20

    Google Scholar 

  • Deville JC (2004) Calage, calage généralisé et hypercalage. Technical report Insee Paris

  • Deville JC, Dupont F (1993) Non-réponse: principes et méthodes. Actes des Journées de Méthodologie statistique. Insee, Paris, pp 53–70

    Google Scholar 

  • Deville JC, Särndal CE (1992) Calibration estimators in survey sampling. J Am Stat Assoc 87:376–382

    Article  MathSciNet  Google Scholar 

  • Dupont F (1994) Calibration used as a nonresponse adjustment, studies in classification, data analysis, and knowledge organization. In: Diday E (ed) New approaches in classification and data analysis. Springer, Berlin, pp 539–548

    Chapter  Google Scholar 

  • Dupont F (1996) Calage et redressement de la non-réponse totale : Validité de la pratique courante de redressement et comparaison des méthodes alternatives pour l’enquête sur la consommation alimentaire de 1989. In: Actes des journées de méthodologie statistique, 15 et 16 décembre 1993, Insee-Méthodes No 56-57-58, Complément, pp 9–42

  • Folsom RE, Singh AC (2000) The generalized exponential model for design weight calibration for extreme values, nonresponse and poststratification. In: Section on survey research methods, American Statistical Association, pp 598–603

  • Graf M (2016) Tight calibration of survey weights. institut de Statistique, Université de Neuchâtel

  • Guandalini A, Tillé Y (2017) Design-based estimators calibrated on estimated totals from multiple surveys. Int Stat Rev 85:250–269

    Article  MathSciNet  Google Scholar 

  • Haziza D, Beaumont JF (2017) Construction of weights in surveys: a review. Stat Sci 32(2):206–226

    Article  MathSciNet  Google Scholar 

  • Huang ET, Fuller WA (1978) Non-negative regression estimation for sample survey data. In: Proceedings of the social statistics section of the American Statistical Association, pp 300–305

  • Iannacchione VG, Milne JG, Folsom RE (1991) Response probability weight adjustments using logistic regression. In: Proceedings of the survey research methods section. American Statistical Association, pp 637–42

  • Kott PS (2006) Using calibration weighting to adjust for nonresponse and coverage errors. Surv Methodol 32:133–142

    Google Scholar 

  • Kott PS, Liao D (2012) Providing double protection for unit nonresponse with a nonlinear calibration-weighting routine. Surv Res Methods 6:105–111

    Google Scholar 

  • Le Guennec J, Sautory O (2002) CALMAR2: une nouvelle version de la macro CALMAR de redressement d’échantillon par calage. Actes des Journées de Méthodologie. Insee, Paris, pp 33–38

    Google Scholar 

  • Lesage É (2011) The use of estimating equations to perform a calibration on complex parameters. Surv Methodol 37(1):103–108

    Google Scholar 

  • Lesage É, Haziza D, D’Haultfoeuille X (2019) A cautionary tale on instrumental calibration for the treatment of nonignorable unit nonresponse in surveys. J Am Stat Assoc 114:906–915

    Article  MathSciNet  Google Scholar 

  • Lundström S, Särndal CE (1999) Calibration as a standard method for treatment of nonresponse. J Off Stat 15:305–327

    Google Scholar 

  • Montanari GE, Ranalli MG (2005) Nonparametric model-calibration estimation in survey sampling. J Am Stat Assoc 100:1429–1442

    Article  MathSciNet  Google Scholar 

  • Rebecq A (2017) icarus: Calibrates and Reweights Units in Samples, likelihood, estimation. R package version (3)

  • Rebecq A (2019) Méthodes de sondage pour les données massives. PhD thesis, Université Paris Nanterre

  • Rényi A (1961) On measures of entropy and information. In: Proceedings of the 4th Berkeley symposium on mathematical statistics and probability, University of California Press, Berkeley, vol 1, pp 547–561

  • Särndal CE, Lundström S (2005) Estimation in surveys with nonresponse. Wiley, New York

    Book  Google Scholar 

  • Vallée AA, Tillé Y (2019) Linearization for variance estimation by means of sampling indicators: application to nonresponse. Int Stat Rev 87(2):347–367

    Article  MathSciNet  Google Scholar 

  • Vanderhoeft C (2001) Generalised calibration at Statistics Belgium SPSS module gCALIBS and current practices. Technical report, Statistics Belgium Working Paper No. 3

  • Wu C, Sitter RR (2001) A model-calibration approach to using complete auxiliary information from survey data. J Am Stat Assoc 96:185–193

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institut de statistique, Université de Neuchâtel, Av. de Bellevaux 51, 2000, Neuchâtel, Switzerland

    Denis Devaud & Yves Tillé

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  1. Denis Devaud
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  2. Yves Tillé
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Correspondence to Denis Devaud.

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This rejoinder refers to the comments available at: https://doi.org/10.1007/s11749-019-00682-2; https://doi.org/10.1007/s11749-019-00683-1; https://doi.org/10.1007/s11749-019-00684-0; https://doi.org/10.1007/s11749-019-00686-y; https://doi.org/10.1007/s11749-019-00687-x.

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Devaud, D., Tillé, Y. Rejoinder on: Deville and Särndal’s calibration: revisiting a 25-year-old successful optimization problem. TEST 28, 1087–1091 (2019). https://doi.org/10.1007/s11749-019-00685-z

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