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.

中文翻译：

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DPER：随机缺失数据的有效参数估计

在过去的几十年中，缺失数据问题得到了广泛的研究，并且在统计或生物信息学等不同领域有各种应用。尽管已经开发了许多方法来应对这一挑战，但其中大多数是插补技术，需要对数据进行多次迭代才能产生收敛。此外，此类方法可能会给估计参数引入额外的偏差和噪声。在这项工作中，我们提出了新的算法，以在一些温和的假设下找到一类/多类随机缺失数据集的最大似然估计（MLE）。由于计算是直接的，没有任何插补，我们的算法不需要对数据进行多次迭代，因此有望在保持卓越的估计性能的同时比其他方法更省时。