Missing Data

Missing data is a pervasive problem across numerous scientific domains, hindering accurate analysis and reliable model building. Current research focuses on developing robust methods that directly handle missing values, rather than relying on imputation, employing techniques like weighted linear discriminant analysis, optimal transport for causal structure learning, and deep learning architectures such as transformers and generative adversarial networks. These advancements are crucial for improving the accuracy and interpretability of machine learning models in various applications, from medical diagnosis and remote sensing to manufacturing and traffic prediction, where incomplete data is frequently encountered.