# An Empirical Comparison of Multiple Imputation Methods for Categorical Data

arXiv: https://arxiv.org/abs/1508.05918 · HTML: https://mlsanity.com/paper/1508.05918/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2015.

## Built on by (most cited first, 8 of 8)

- [Classification of datasets with imputed missing values: does imputation quality matter?](https://mlsanity.com/paper/2206.08478/) (2022)
- [Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework](https://mlsanity.com/paper/2202.13734/) (2022)
- [Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison](https://mlsanity.com/paper/2103.09316/) (2021)
- [Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies](https://mlsanity.com/paper/2401.09602/) (2024)
- [A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative](https://mlsanity.com/paper/2206.06444/) (2022)
- [IRTCI: Item Response Theory for Categorical Imputation](https://mlsanity.com/paper/2302.04165/) (2023)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [Predicting missing values: A good idea?](https://mlsanity.com/paper/2605.03733/) (2026)

## Abstract (arXiv)

Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the database. Researchers have developed a variety of default routines to implement multiple imputation; however, there has been limited research comparing the performance of these methods, particularly for categorical data. We use simulation studies to compare repeated sampling properties of three default multiple imputation methods for categorical data, including chained equations using generalized linear models, chained equations using classification and regression trees, and a fully Bayesian joint distribution based on Dirichlet Process mixture models. We base the simulations on categorical data from the American Community Survey. In the circumstances of this study, the results suggest that default chained equations approaches based on generalized linear models are dominated by the default regression tree and Bayesian mixture model approaches. They also suggest competing advantages for the regression tree and Bayesian mixture model approaches, making both reasonable default engines for multiple imputation of categorical data. A supplementary material for this article is available online.