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Related Experiment Videos

The relationship between hot-deck multiple imputation and weighted likelihood

M Reilly1, M Pepe

  • 1Department of Statistics, University College Dublin, Belfield, Ireland.

Statistics in Medicine
|January 15, 1997
PubMed
Summary
This summary is machine-generated.

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Hot-deck imputation, a popular method for incomplete data, is shown to be equivalent to the mean-score method. This finding allows for valid statistical inference using a provided variance expression.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Hot-deck imputation is a widely used technique for handling missing data.
  • Standard variance estimators are often inappropriately applied with hot-deck imputation.
  • A variance expression for hot-deck imputation in regression models with missing covariates has been lacking.

Purpose of the Study:

  • To derive and provide a variance expression for hot-deck imputation in regression models.
  • To establish the asymptotic equivalence between hot-deck imputation and the mean-score method.
  • To demonstrate the practical application and validity of this approach using clinical data.

Main Methods:

  • Derivation of asymptotic properties for hot-deck imputation with categorical observed variables and continuous incomplete covariates.

Related Experiment Videos

  • Establishing the asymptotic equivalence to the mean-score method, a weighted likelihood approach.
  • Conducting simulation studies to compare performance in small samples and with few imputations.

    • Main Results:

    • Hot-deck imputation is asymptotically equivalent to the mean-score method under missing at random (MAR) conditions.
    • Simulation studies show comparable performance between hot-deck and mean-score methods in small samples.
    • A validated variance expression for hot-deck imputation is provided, enabling straightforward inference.

    Conclusions:

    • Hot-deck imputation can be reliably implemented using the mean-score method, a weighted likelihood technique.
    • The derived variance expression facilitates valid statistical inference for regression models with missing covariates.
    • The equivalence is confirmed through analysis of three clinical datasets with substantial missing covariate data.