Exact balanced random imputation for sample survey data

Guillaume Chauvet (1) , Wilfried Do Paco (2)

1 IRMAR - Institut de Recherche Mathématique de Rennes
2 INSEE

Surveys usually suffer from non-response, which decreases the effective sample size. Item non-response is typically handled by means of some form of random imputation if we wish to preserve the distribution of the imputed variable. This leads to an increased variability due to the imputation variance, and several approaches have been proposed for reducing this variability. Balanced imputation consists in selecting residuals at random at the imputation stage, in such a way that the imputation variance of the estimated total is eliminated or at least significantly reduced. In this work, we propose an implementation of balanced random imputation which enables to fully eliminate the imputation variance. Following the approach in Cardot et al. (2013), we consider a regularized imputed estimator of a total and of a distribution function, and we prove that they are consistent under the proposed imputation method. Some simulation results support our findings.

Mots clés: cube method; distribution function; mean-square consistency; imputation mechanism; imputation model; balanced imputation; regularized estimator

Domaines: Méthodologie [stat.ME]; Théorie [stat.TH]

Guillaume Chauvet, Wilfried Do Paco. Exact balanced random imputation for sample survey data. Computational Statistics and Data Analysis, 2018, 128, pp.1-16. ⟨10.1016/j.csda.2018.06.006⟩. ⟨hal-01353764v2⟩