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Gaussian Copula-based Imputation Method for Mixed Data with Non-Ignorable Missing Values

Year: 2026

Type: article

Abstract: issing data with non-ignorable mechanisms (MNAR) poses a significant challenge for the analysis of mixed-type datasets. To address this, we propose a Corrected Marginal Gaussian Copula (CM-GC) imputation framework. The method first employs an Auxiliary Empirical Likelihood (AEL) approach within a semiparametric exponential tilting model to consistently estimate the propensity scores under MNAR. These scores are used to construct a Horvitz-Thompson estimator, recovering unbiased marginal distributions for the missing variables. Subsequently, the corrected marginals are linked via a Gaussian copula, projecting the data into a latent multivariate normal space.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18414974

Author: Tuerde, Mulati

Institution: Xinjiang University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7126039770

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