Title : Multiple Imputation and Missing Data
Speaker: Prof. Stef van Buuren, University of Utrecht, Holland
Moderator: Alfred H. Balch
Multiple imputation is a principled approach to deal with missing data. In this lecture, we discuss the evolution of the technology since the 70’s until now, and identify new problems that surface in the context of big data, machine learning, flowing data, network problems and other problems of current interest.
Stef van Buuren is Professor of Statistical Analysis of Incomplete Data at the University of Utrecht and Principal Scientist at the Netherlands Organisation for Applied Scientific Research TNO in Leiden. His interests include the analysis of incomplete data, child growth and development, computational statistics, measurement and individual causal effects. Van Buuren is the inventor of the MICE algorithm for multiple imputation of missing data. He created the growth charts used in the Dutch child health care system, and designed the D-score, a new system for expressing child development on a quantitative scale. He consults for the World Health Organization and the Bill & Melinda Gates Foundation.