Multiple imputation of discrete and continuous data by fully conditional specification

By S. van Buuren. 2007

1,066 citations recorded by the source corpus.

Subfields: Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Eric B. Laber
2. Daniel J. Lizotte
3. Michael R. Kosorok
4. Susan M. Shortreed
5. Susan A. Murphy
6. Yang Yuan
7. Karin G. M. Groothuis-Oudshoorn
8. Joelle Pineau

Citations in the corpus

• Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “We first use fully conditional specification (FCS) (van Buuren et al. 2006; van Buuren 2007 ) to estimate all missing variables.”
• MICE: Multivariate Imputation by Chained Equations in R (2011, methodology): “…sequential regressions (Raghunathan et al. 2001), ordered pseudo-Gibbs sampler (Heckerman et al. 2001), partially incompatible MCMC (Rubin 2003), iterated univariate imputation (Gelman 2004), MICE (van Buuren and Oudshoorn 2000; van Buuren and Groothuis-Oudshoorn 2011) and FCS (van Buuren 2007).”
• Multiple Imputation Using SAS Software (2011, methodology): “For data sets with arbitrary missing patterns, a fully conditional specification (FCS) method can also be used to impute missing values for both continuous and classification variables (Brand 1999; van Buuren 2007).”