Imputation in the survey on living conditions. The Spanish “European Statistics on Income and Living Conditions” (EU-SILC) is one of the statistical operations that has been harmonised to EU standards. In this household survey there are two kinds of non-response: unit non-response (one or several household or individual questionnaires are missing) and item non-response (no questionaire is missing but some variables are). The main target variable is the total household income, which is defined as the aggregate of different income components. Components with missing values are imputed when they cannot be estimated with the help of other variables or other information in the questionnaire of the current or previous surveys. The procedure applied to the data preserves the variability of the variables and the correlations between them. The statistical software used for imputation is the IVEware. The IVEware implements a multivariate model involving a multiple regression sequence where imputation is carried out variable by variable generating draws from the predictive distribution specified by the regression model. An iterative imputation scheme is used, updating previous imputed values in order to better preserve the correlation among variables.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Sun, Yilun; Wang, Lu: Stochastic tree search for estimating optimal dynamic treatment regimes (2021)
  2. Zha, Ruochen; Harel, Ofer: Power calculation in multiply imputed data (2021)
  3. Bowen, Claire Mckay; Liu, Fang: Comparative study of differentially private data synthesis methods (2020)
  4. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  5. Tao, Yebin; Wang, Lu: Adaptive contrast weighted learning for multi-stage multi-treatment decision-making (2017)
  6. Beata Nowok and Gillian Raab and Chris Dibben: synthpop: Bespoke Creation of Synthetic Data in R (2016) not zbMATH
  7. Bishop, Brenden; Jeon, Minjeong: Book review of: T. Raghunathan, Missing data analysis in practice (2016)
  8. Guo, Ying; Prof. Little, Roderick J.: Bayesian multiple imputation for assay data subject to measurement error (2013)
  9. Pannekoek, Jeroen; Shlomo, Natalie; De Waal, Ton: Calibrated imputation of numerical data under linear edit restrictions (2013)
  10. Daniel, Rhian M.; Kenward, Michael G.: A method for increasing the robustness of multiple imputation (2012)
  11. Drechsler, Jörg: Multiple imputation in practice -- a case study using a complex German establishment survey (2011)
  12. Little, Roderick: Calibrated Bayes, for statistics in general, and missing data in particular (2011)
  13. Schenker, Nathaniel: Discussion of “Calibrated Bayes, for statistics in general, and missing data in particular” by R. Little (2011)
  14. White, Ian R.; Daniel, Rhian; Royston, Patrick: Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables (2010)
  15. Juned Siddique; Ofer Harel: MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors (2009) not zbMATH
  16. Méndez Martín, José María: Imputation in the survey on living conditions (2008)
  17. Gretchen Carrigan and Adrian Barnett and Annette Dobson and Gita Mishra: Compensating for Missing Data from Longitudinal Studies Using WinBUGS (2007) not zbMATH
  18. Rendtel, Ulrich: The 2005 Plenary Meeting on “Missing data and measurement error” (2006)
  19. van Buuren, S.; Brand, J. P. L.; Groothuis-Oudshoorn, C. G. M.; Rubin, D. B.: Fully conditional specification in multivariate imputation (2006)