DaSSWeb | Imputation of missing values revisited

Speaker
Matthias Templ
FHNW University of Applied Sciences and Arts Northwestern Switzerland
School of Business
Institute for Competitiveness and Communication

Abstract
This presentation explores imputation techniques for missing values. It discusses the concepts of common single and multiple imputation methods including modern methods of robust imputation, imputation based on deep learning and imputation for complex data and it critically review some of the paradigms in imputation literature. Additionally, the problems of outliers are discussed.

Short Bio
Matthias Templ is a Professor at the Institute for Competitiveness and Communication at the University of Applied Sciences and Arts Northwestern Switzerland in Olten, and a lecturer at ETH Zurich and the Vienna University of Technology, where he was awarded the venia docendi (habilitation) in statistics. His main research interests include computational statistics, compositional data analysis, robust statistics, imputation of missing values and anonymization of data. He is the Editor-in-Chief of the Austrian Journal of Statistics and author of five books including Visualization and Imputation of Missing Values. He is also an author and the maintainer of the CRAN Task View on Official Statistics and of several R packages, e.g. the VIM package for visualization and imputation of missing values.

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