  Missing data imputation forms the first critical step of many data analysis
pipelines. The challenge is greatest for mixed data sets, including real,
Boolean, and ordinal data, where standard techniques for imputation fail basic
sanity checks: for example, the imputed values may not follow the same
distributions as the data. This paper proposes a new semiparametric algorithm
to impute missing values, with no tuning parameters. The algorithm models mixed
data as a Gaussian copula. This model can fit arbitrary marginals for
continuous variables and can handle ordinal variables with many levels,
including Boolean variables as a special case. We develop an efficient
approximate EM algorithm to estimate copula parameters from incomplete mixed
data. The resulting model reveals the statistical associations among variables.
Experimental results on several synthetic and real datasets show superiority of
our proposed algorithm to state-of-the-art imputation algorithms for mixed
data.
