Corpus ID: 10201308

A multivariate technique for multiply imputing missing values using a sequence of regression models

@article{Raghunathan2001AMT,
  title={A multivariate technique for multiply imputing missing values using a sequence of regression models},
  author={T. E. Raghunathan and J. Lepkowski and John Van Hoewyk and P. Solenberger},
  journal={Survey Methodology},
  year={2001},
  volume={27},
  pages={85-95}
}
  • T. E. Raghunathan, J. Lepkowski, +1 author P. Solenberger
  • Published 2001
  • Mathematics
  • Survey Methodology
  • This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation… CONTINUE READING
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    References

    SHOWING 1-10 OF 30 REFERENCES
    Maximum likelihood estimation for mixed continuous and categorical data with missing values
    • 228
    Performing likelihood ratio tests with multiply-imputed data sets
    • 282
    Missing data imputation using the multivariate t distribution
    • 58
    Multiple Imputation After 18+ Years
    • 3,007
    • PDF
    A Split Questionnaire Survey Design
    • 159
    Jackknife variance estimation with survey data under hot deck imputation
    • 284
    Inference from Iterative Simulation Using Multiple Sequences
    • 10,175
    • PDF
    The calculation of posterior distributions by data augmentation
    • 3,767
    • Highly Influential