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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Variable selection in the presence of missing data: resampling and imputation.

Qi Long1, Brent A Johnson2

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA qlong@emory.edu.

Biostatistics (Oxford, England)
|February 20, 2015
PubMed
Summary

A new method combining bootstrap imputation and stability selection (BI-SS) effectively handles missing data for variable selection. This approach performs best in simulations, offering robust variable selection for both low- and high-dimensional data.

Keywords:
Bootstrap imputationMissing dataResamplingStability selectionVariable selection

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Area of Science:

  • Statistics
  • Data Science
  • Machine Learning

Background:

  • Missing data presents challenges for variable selection methods.
  • Existing methods require tailoring to specific missing data mechanisms and imputation techniques.

Purpose of the Study:

  • To develop and evaluate a general resampling approach for variable selection in the presence of missing data.
  • To investigate the performance of a method combining bootstrap imputation and stability selection (BI-SS) under the missing at random mechanism.

Main Methods:

  • The study proposes a general resampling approach, BI-SS, integrating bootstrap imputation with stability selection.
  • Stability selection, originally for complete data, is adapted for use with imputed datasets.

Main Results:

  • Extensive simulations show BI-SS performs best or near-best across various settings.
  • The BI-SS method demonstrates robustness and insensitivity to tuning parameter choices.
  • Performance is validated for both low-dimensional and high-dimensional datasets.

Conclusions:

  • BI-SS offers a powerful and versatile solution for variable selection with missing data.
  • The approach is effective for diverse statistical and machine learning applications.
  • The method's reliability is confirmed through simulations and real-world data examples.