4 papers
LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
Stef van Buuren
Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generating answers under incomplete c…
HIMCE: High-dimensional multiple imputation via covariance-mode updating for neuroimaging and spatiotemporal blocks
Hsin-Hsiung Huang, Stef van Buuren
High-dimensional neuroimaging and spatiotemporal blocks often contain structured missingness from acquisition artifacts, preprocessing failures, and sensor dropout. Multiple imputa…
Predicting missing values: A good idea?
Stef van Buuren
Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach provides accurate point estimat…
Sensitivity analysis for multivariable missing data using multiple imputation: a tutorial
Cattram D Nguyen, Katherine J Lee, Ian R White +2
Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable m…