A new double hot-deck imputation method for missing values under boundary conditions

Abstract

In surveys, logical boundaries among variables or among waves of surveys make imputation of missing values complicated. We propose a new regression-based multiple imputation method to deal with survey non-responses with two-sided logical boundaries. This imputation method automatically satisfies the boundary conditions without an additional acceptance/rejection procedure and utilizes the boundary information to derive an imputed value and to determine the suitability of the imputed value. Simulation results show that our new imputation method outperforms the existing imputation methods for both mean and quantile estimations regardless of missing rates, error distributions, and missing-mechanisms. We apply our method to impute the self-reported variable "years of smoking" in successive health screenings of Koreans.