  The missing data problem has been broadly studied in the last few decades and
has various applications in different areas such as statistics or
bioinformatics. Even though many methods have been developed to tackle this
challenge, most of those are imputation techniques that require multiple
iterations through the data before yielding convergence. In addition, such
approaches may introduce extra biases and noises to the estimated parameters.
In this work, we propose novel algorithms to find the maximum likelihood
estimates (MLEs) for a one-class/multiple-class randomly missing data set under
some mild assumptions. As the computation is direct without any imputation, our
algorithms do not require multiple iterations through the data, thus promising
to be less time-consuming than other methods while maintaining superior
estimation performance. We validate these claims by empirical results on
various data sets of different sizes and release all codes in a GitHub
repository to contribute to the research community related to this problem.
