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Bayesian nonparametric multiple imputation of partially observed data with ignorable nonresponse

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  • Susan M. Paddock

Abstract

We present a new, nonparametric Bayesian method for multiple imputation of partially observed data for which the pattern of missingness is arbitrary and the data are missing at random with ignorable nonresponse with respect to the model specification. Motivation for the method is provided, followed by an overview of Pólya trees and their application to multiple imputation, and a comparison of the new method to existing approaches is presented. The method is illustrated on a dataset of colleges and universities in the United States. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Susan M. Paddock, 2002. "Bayesian nonparametric multiple imputation of partially observed data with ignorable nonresponse," Biometrika, Biometrika Trust, vol. 89(3), pages 529-538, August.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:3:p:529-538
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    Cited by:

    1. Marco Di Zio & Ugo Guarnera, 2008. "A multiple imputation method for non-Gaussian data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 75-90.
    2. Adam J. Branscum & Timothy E. Hanson, 2008. "Bayesian Nonparametric Meta‐Analysis Using Polya Tree Mixture Models," Biometrics, The International Biometric Society, vol. 64(3), pages 825-833, September.
    3. Ma, Zichen & Hanson, Timothy E., 2020. "Bayesian nonparametric test for independence between random vectors," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).

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