Multiple Imputation for Missing Data: Concepts and New Development

By Yang Yuan. 2000

582 citations recorded by the source corpus.

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Citations in the corpus

Rebutting Existing Misconceptions About Multiple Imputation as a Method for Handling Missing Data (2019, methodology): “On the other hand, the procedure in SPSS lacks some important options that are available in less user-friendly software, such as the mice procedure (Van Buuren & Groothuis-Oudshoorn, 2011) in R (R Development Core Team, 2013), the mi procedure in Stata 14.0 (StataCorp, 2015), and the PROC MI procedu”
Labeling and Cumulative Disadvantage (2012, methodology): “The validity of the multiple imputation model was assessed by examining timeplots and autocorrelation plots (see Allison, 2001, and Yuan, 2000), assessing the posterior parameter estimates degrees of freedom (see Gullion et al., 2008), and examining pre- and postimputation descriptive statistics…”
Missing data: our view of the state of the art. (2002, methodology): “A new SAS procedure, PROC MI (Y. C. Yuan, 2000 ), brings the method used by NORM into the popular SAS environment.”
Examining the Importance of Intentionally Structuring the Youth Sport Context to Facilitate Positive Youth Development (2016, methodology): “Missing data were replaced with multiple imputations (Yuan, 2010).”
Using Nonfinancial Measures to Assess Fraud Risk (2009, methodology): “…data sets are analyzed 11 When we perform our tests of H2 without imputing missing controls, the multivariate results are qualitatively similar for DIFF and EMPLOYEE DIFF (p’s = .01). using standard procedures (e.g., standard logistic regression) and the results are combined (Yuan 2007).12 IV.”
The relationship between non-suicidal self-injury and suicidality (2016, background): “This procedure imputes missing values several times based on random selection of different estimates of the underlying distributions in the population (Yuan, 2000).”
Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ (2003, methodology): “Programs for creating MI’s include NORM (SCHAFER, 1999), the S+MissingData library in S-PLUS (SCHIMERT et al., 2001), SAS PROC MI (YUAN, 2000), Amelia (KING et al., 2001), SOLAS (STATISTICAL SOLUTIONS, 2002), MICE (VAN BUUREN and OUDSHOORN, 1999), and IVEware (RAGHUNATHAN, SOLENBERGER and VAN…”
Comparison of Four Methods for Handing Missing Data in Longitudinal Data Analysis through a Simulation Study (2014, background): “Finally, the results of analyses are combined [35] [36].”