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Found 2 papers, 2 papers with code
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison
We conduct extensive simulation studies based on a subsample of the American Community Survey to compare the repeated sampling properties of four machine learning based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders.
A Comparative Study of Imputation Methods for Multivariate Ordinal Data
In certain settings, MI using multinomial logistic regression models is able to achieve comparable performance, depending on the missing data mechanism and amount of missing data.
Methodology Applications
