  Many real-world datasets contain missing entries and mixed data types
including categorical and ordered (e.g. continuous and ordinal) variables.
Imputing the missing entries is necessary, since many data analysis pipelines
require complete data, but this is challenging especially for mixed data. This
paper proposes a probabilistic imputation method using an extended Gaussian
copula model that supports both single and multiple imputation. The method
models mixed categorical and ordered data using a latent Gaussian distribution.
The unordered characteristics of categorical variables is explicitly modeled
using the argmax operator. The method makes no assumptions on the data
marginals nor does it require tuning any hyperparameters. Experimental results
on synthetic and real datasets show that imputation with the extended Gaussian
copula outperforms the current state-of-the-art for both categorical and
ordered variables in mixed data.
