  Missing data theory deals with the statistical methods in the occurrence of
missing data. Missing data occurs when some values are not stored or observed
for variables of interest. However, most of the statistical theory assumes that
data is fully observed. An alternative to deal with incomplete databases is to
fill in the spaces corresponding to the missing information based on some
criteria, this technique is called imputation. We introduce a new imputation
methodology for databases with univariate missing patterns based on additional
information from fully-observed auxiliary variables. We assume that the
non-observed variable is continuous, and that auxiliary variables assist to
improve the imputation capacity of the model. In a fully Bayesian framework,
our method uses a flexible mixture of multivariate normal distributions to
model the response and the auxiliary variables jointly. Under this framework,
we use the properties of Gaussian Cluster-Weighted modeling to construct a
predictive model to impute the missing values using the information from the
covariates. Simulations studies and a real data illustration are presented to
show the method imputation capacity under a variety of scenarios and in
comparison to other literature methods.
