Semiparametric Imputation Using Conditional Gaussian Mixture Models under Item Nonresponse

TL;DR

This paper introduces a flexible semiparametric imputation method using a conditional Gaussian mixture model that effectively handles high-dimensional covariates and item nonresponse in survey data, improving robustness and accuracy.

Contribution

It proposes a novel semiparametric imputation approach with a conditional Gaussian mixture model that reduces approximation error and is suitable for high-dimensional data.

Findings

Achieves lower approximation error than Gaussian mixture models.

Applicable to high-dimensional covariate problems with penalty functions.

Demonstrated on Korean survey data with promising results.

Abstract

Imputation is a popular technique for handling item nonresponse in survey sampling. Parametric imputation is based on a parametric model for imputation and is less robust against the failure of the imputation model. Nonparametric imputation is fully robust but is not applicable when the dimension of covariates is large due to the curse of dimensionality. Semiparametric imputation is another robust imputation based on a flexible model where the number of model parameters can increase with the sample size. In this paper, we propose another semiparametric imputation based on a more flexible model assumption than the Gaussian mixture model. In the proposed mixture model, we assume a conditional Gaussian model for the study variable given the auxiliary variables, but the marginal distribution of the auxiliary variables is not necessarily Gaussian. We show that the proposed mixture model achieves a lower approximation error bound to any unknown target density than the Gaussian mixture model in terms of the Kullback-Leibler divergence. The proposed method is applicable to high dimensional covariate problem by including a penalty function in the conditional log-likelihood function. The proposed method is applied to 2017 Korean Household Income and Expenditure Survey conducted by Statistics Korea. Supplementary material is available online.