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A note on approximate Bayesian bootstrap imputation

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  • J. K. Kim

Abstract

The approximate Bayesian bootstrap is suggested by Rubin & Schenker (1986) as a way of generating multiple imputations when the original sample can be regarded as independently and identically distributed and the response mechanism is ignorable. We investigate the finite sample properties of the variance estimator when the approximate Bayesian bootstrap method is used and show that the bias is not negligible for moderate sample sizes. A modification of the method is proposed for reducing the bias of the variance estimator. The proposed method is asymptotically equivalent to the approximate Bayesian bootstrap method but has better finite sample properties. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • J. K. Kim, 2002. "A note on approximate Bayesian bootstrap imputation," Biometrika, Biometrika Trust, vol. 89(2), pages 470-477, June.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:470-477
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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. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    3. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    4. Demirtas, Hakan & Arguelles, Lester M. & Chung, Hwan & Hedeker, Donald, 2007. "On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 4064-4068, May.

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