Iterative Imputation

Iterative imputation is a data preprocessing technique that repeatedly refines estimates of missing values in datasets by iteratively modeling the relationships between variables. Current research emphasizes improving imputation accuracy through advanced algorithms like k-nearest neighbors and integrating iterative imputation within broader frameworks such as matrix decomposition and probabilistic forecasting models, often incorporating automatic model selection. This approach is crucial for handling missing data in diverse fields, from biomedicine (e.g., integrating multi-omics data) to renewable energy forecasting, enabling more robust analyses and improved predictive modeling where complete datasets are unavailable. The effectiveness of iterative imputation, particularly when combined with techniques like missing-indicator methods, is consistently demonstrated across various applications and datasets.