Missingness Pattern

Missingness patterns, the ways in which data are missing in a dataset, significantly impact the accuracy and reliability of analyses. Current research focuses on understanding and addressing various missingness mechanisms (e.g., Missing Completely At Random, Missing At Random, Missing Not At Random), developing robust prediction models that are less sensitive to shifts in missingness, and creating effective imputation techniques, including those leveraging deep learning architectures like variational autoencoders and attention mechanisms. This work is crucial for improving the reliability of machine learning models across diverse fields, from healthcare (e.g., analyzing electronic health records) to finance (e.g., credit scoring) and beyond, where incomplete data is ubiquitous.