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Imputation Estimators Partially Correct for Model Misspecification

Author

Listed:
  • Minin Vladimir N.

    (University of Washington)

  • O'Brien John D.

    (University of Oxford)

  • Seregin Arseni

    (University of Washington)

Abstract

Inference problems with incomplete observations often aim at estimating population properties of unobserved quantities. One simple way to accomplish this estimation is to impute the unobserved quantities of interest at the individual level and then take an empirical average of the imputed values. We show that this simple imputation estimator can provide partial protection against model misspecification. We illustrate imputation estimators’ robustness to model specification on three examples: mixture model-based clustering, estimation of genotype frequencies in population genetics, and estimation of Markovian evolutionary distances. In the final example, using a representative model misspecification, we demonstrate that in non-degenerate cases, the imputation estimator dominates the plug-in estimate asymptotically. We conclude by outlining a Bayesian implementation of the imputation-based estimation.

Suggested Citation

  • Minin Vladimir N. & O'Brien John D. & Seregin Arseni, 2011. "Imputation Estimators Partially Correct for Model Misspecification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, April.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:17
    DOI: 10.2202/1544-6115.1650
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    References listed on IDEAS

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    1. Chen, Yi-Hau & Chatterjee, Nilanjan & Carroll, Raymond J., 2009. "Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 220-233.
    2. Chris Fraley & Adrian E. Raftery, 2003. "Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST," Journal of Classification, Springer;The Classification Society, vol. 20(2), pages 263-286, September.
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