CU Boulder News & Events

Multiple Imputation and Missing Data

Missing data can plague researchers in many scenarios, arising from incomplete surveys, experimental objects broken or destroyed, or data collection/computational errors. This short course will ...
BMJ Open

Handling missingness in electronic health record laboratory data for secondary use: a decision-making process

Electronic health record (EHR) systems have become widely adopted, prompting a growing interest in leveraging EHR data for ...
University of Bristol

Advanced Multiple Imputation Methods to Deal with Missing Data

Multiple imputation is a principled approach to account for missing data in analyses where valid results depends on careful construction of the imputation model. The potential for misspecification of ...
BIOENGINEER

Simple beats deep: linear interpolation outperforms AI models when real-world clinical data goes missing

A new benchmark study shows that simple linear imputation methods outperform deep learning models when realistic ...
CBS

On Kalman filtering and variances after single and multiple imputation

Using Kalman equations, we derive straightforward formulas for the total imputation variance for several imputation methods commonly used in regression analysis and (un)equal probability sampling ...
Harvard Business School

Multiple Imputation Using Gaussian Copulas

Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative ...
San Francisco Fed

Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning

This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is ...