Review for Handling Missing Data with special missing mechanism

arXiv:2404.04905v1 Announce Type: cross
Abstract: Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes. Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world. Three missing mechanisms are defined in the literature: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At …

abstract arxiv challenge cs.ai cs.lg data data science decision making processes review science stat.me tabular tabular data type understanding world