Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence

@article{Murray2014MultipleIO,
  title={Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence},
  author={Jared Murray and Jerome P. Reiter},
  journal={Journal of the American Statistical Association},
  year={2014},
  volume={111},
  pages={1466 - 1479}
}
  • Jared MurrayJ. Reiter
  • Published 2 October 2014
  • Mathematics
  • Journal of the American Statistical Association
ABSTRACT We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous and categorical variables by (1) modeling the means… 

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