  We present a Bayesian mixture model for estimating the joint distribution of
mixed ordinal, nominal, and continuous data conditional on a set of fixed
variables. The model uses multivariate normal and categorical mixture kernels
for the random variables. It induces dependence between the random and fixed
variables through the means of the multivariate normal mixture kernels and via
a truncated local Dirichlet process. The latter encourages observations with
similar values of the fixed variables to share mixture components. Using a
simulation of data fusion, we illustrate that the model can estimate underlying
relationships in the data and the distributions of the missing values more
accurately than a mixture model applied to the random and fixed variables
jointly. We use the model to analyze consumers' reading behaviors using a quota
sample, i.e., a sample where the empirical distribution of some variables is
fixed by design and so should not be modeled as random, conducted by the book
publisher HarperCollins.
