Flexible Imputation of Missing Data

By S. van Buuren. 2012

389 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f456e1fe56d71b0a12f588fc16db88cd2d46e1b5

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In Statistics and Probability

Citations in the corpus

Multiple Imputation of Missing Data for Multilevel Models (2018, background): “This is commonly referred to as “passive imputation” (Royston, 2004; van Buuren, 2012).”
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data (2018, background): “as strong implications for downstream modeling in both fairness [20] and causal inference [41]. Our model outputs counterfactual outcomes for both A and T, whichcouldbeusedforfairmissingdataimputation[45,47]. Thiscouldinturnenabletheapplicationofsimplermethods like supervised learning to interventio”
HeMIS: Hetero-Modal Image Segmentation (2016, background): “it is common to have one or more modalities to be missing for a given instance. This problem is not new, and the subject of missing data analysis has spawned an immense literature in statistics (e.g. [22]). In medical imaging, a number of approaches have been proposed, some of which require to re-tr”
Rebutting Existing Misconceptions About Multiple Imputation as a Method for Handling Missing Data (2019, methodology): “…there are two main approaches for generating multiply imputed data sets, namely the joint modeling approach (Schafer, 1997; Van Buuren, 2012, pp. 105 – 108) and the fully conditional specification approach (Van Buuren, 2012, pp. 116 – 118; Van Buuren, Brand, Groothuis-Oudshoorn, & Rubin, 2006).”
Flexible Imputation of Missing Data, 2nd ed. (2019, background): “The first edition of this book (van Buuren 2012) has been popular and wellreceived in the statistics and applied research communities.”