  We consider the matrix completion problem of recovering a structured low rank
matrix with partially observed entries with mixed data types. Vast majority of
the solutions have proposed computationally feasible estimators with strong
statistical guarantees for the case where the underlying distribution of data
in the matrix is continuous. A few recent approaches have extended using
similar ideas these estimators to the case where the underlying distributions
belongs to the exponential family. Most of these approaches assume that there
is only one underlying distribution and the low rank constraint is regularized
by the matrix Schatten Norm. We propose a computationally feasible statistical
approach with strong recovery guarantees along with an algorithmic framework
suited for parallelization to recover a low rank matrix with partially observed
entries for mixed data types in one step. We also provide extensive simulation
evidence that corroborate our theoretical results.
