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Bootstrap inference for multiple imputation under uncongeniality and misspecification.

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This summary is machine-generated.

Multiple imputation is popular for missing data, but can fail when models differ. Certain bootstrap-then-impute methods offer valid statistical inferences, even with uncongeniality and model misspecification.

Keywords:
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Area of Science:

  • Statistics
  • Data Science
  • Biostatistics

Background:

  • Multiple imputation (MI) is a widely used technique for handling missing data in statistical analyses.
  • Rubin's combination rules provide valid frequentist inferences under specific conditions of model congeniality and correct specification.
  • Non-congenial imputation and analysis models can lead to incorrect statistical test sizes and inaccurate confidence interval coverage.

Purpose of the Study:

  • To evaluate recent proposals combining bootstrapping with multiple imputation.
  • To determine the validity of these combined methods under uncongeniality and model misspecification.
  • To identify computationally efficient and statistically valid approaches for handling missing data.

Main Methods:

  • Examined various methods that combine bootstrapping with multiple imputation.
  • Assessed the performance of these methods under conditions of non-congenial imputation and analysis models.
  • Investigated the validity of variance estimation and inference procedures.

Main Results:

  • Imputation followed by bootstrapping generally yields invalid variance estimates when models are uncongenial or misspecified.
  • Certain methods involving bootstrapping followed by imputation demonstrate validity under uncongeniality and misspecification.
  • A specific computationally efficient bootstrap-then-impute method is recommended.

Conclusions:

  • Standard imputation followed by bootstrapping is not robust to uncongeniality or model misspecification.
  • Bootstrap-followed-by-imputation strategies can provide valid inferences in challenging scenarios.
  • The recommended method offers a practical and statistically sound solution for complex missing data problems.