Various Imputation

Various imputation techniques aim to fill in missing data in datasets, improving the reliability and utility of analyses across diverse fields. Current research focuses on developing sophisticated imputation methods, including those based on transformers, diffusion models, and contrastive learning, as well as evaluating the impact of imputation on downstream tasks like model interpretability and prediction accuracy. These advancements are crucial for handling the pervasive problem of missing data in real-world datasets, enabling more robust and reliable analyses in scientific research and practical applications such as healthcare and structural health monitoring. The choice of imputation method significantly impacts results, highlighting the need for careful selection and evaluation based on data characteristics and analytical goals.