Preserving the distribution function in surveys in case of imputation for zero inflated data

Brigitte Gelein (1, 2) , Guillaume Chauvet (2)

1 ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz]
2 IRMAR - Institut de Recherche Mathématique de Rennes

Item non-response in surveys is usually handled by single imputation, whose main objective is to reduce the non-response bias. Imputation methods need to be adapted to the study variable. For instance, in business surveys, the interest variables often contain a large number of zeros. Motivated by a mixture regression model, we propose two imputation procedures for such data and study their statistical properties. We show that these procedures preserve the distribution function if the imputation model is well specified. The results of a simulation study illustrate the good performance of the proposed methods in terms of bias and mean square error.

Mots clés: regression imputation; balanced imputation; imputation model; mixture model; item non response; regression imputation *

Domaines: Méthodologie [stat.ME]

Brigitte Gelein, Guillaume Chauvet. Preserving the distribution function in surveys in case of imputation for zero inflated data. Journal of Statistical Planning and Inference, 2020, 206, pp.84-99. ⟨hal-01879234v2⟩