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

TL;DR

This paper introduces two imputation methods tailored for zero-inflated survey data, ensuring the preservation of the distribution function and demonstrating good bias and mean square error performance.

Contribution

The paper proposes novel imputation procedures for zero-inflated survey data that maintain the distribution function under correct model specification.

Findings

Imputation methods preserve the distribution function when the model is well specified.

Simulation results show low bias and mean square error of the proposed methods.

Methods perform well in handling zero-inflated data in surveys.

Abstract

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.