  The problem of monotone missing data has been broadly studied during the last
two decades and has many applications in different fields such as
bioinformatics or statistics. Commonly used imputation techniques require
multiple iterations through the data before yielding convergence. Moreover,
those approaches may introduce extra noises and biases to the subsequent
modeling. In this work, we derive exact formulas and propose a novel algorithm
to compute the maximum likelihood estimators (MLEs) of a multiple class,
monotone missing dataset when all the covariance matrices of all categories are
assumed to be equal, namely EPEM. We then illustrate an application of our
proposed methods in Linear Discriminant Analysis (LDA). As the computation is
exact, our EPEM algorithm does not require multiple iterations through the data
as other imputation approaches, thus promising to handle much less
time-consuming than other methods. This effectiveness was validated by
empirical results when EPEM reduced the error rates significantly and required
a short computation time compared to several imputation-based approaches. We
also release all codes and data of our experiments in one GitHub repository to
contribute to the research community related to this problem.
