Now showing items 1-3 of 3

    • An Empirical Comparison of Multiple Imputation Methods for Categorical Data 

      Li, Fan; Reiter, Jerome; Akande, Olanrewaju (The American Statistician, 2017-04-03)
      © 2017 American Statistical Association. Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated ...
    • Dirichlet Process Mixture Models for Nested Categorical Data 

      Hu, Jingchen (2015)
      This thesis develops Bayesian latent class models for nested categorical data, e.g., people nested in households. The applications focus on generating synthetic microdata for public release and imputing missing data for ...
    • Simultaneous Edit and Imputation for Household Data with Structural Zeros 

      Akande, Olanrewaju; Barrientos, Andres; Reiter, Jerome
      Multivariate categorical data nested within households often include reported values that fail edit constraints---for example, a participating household reports a child's age as older than his biological parent's age---as ...