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The E-MS Algorithm: Model Selection with Incomplete Data.

Jiming Jiang1, Thuan Nguyen1, J Sunil Rao1

  • 1University of California, Davis, Oregon Health and Science University and University of Miami.

Journal of the American Statistical Association
|January 20, 2016
PubMed
Summary
This summary is machine-generated.

We introduce the E-MS algorithm for selecting statistical models with missing data. This method integrates model selection into the iterative process, improving accuracy and consistency for complex datasets.

Keywords:
backcross experimentsconditional samplingconsistencyconvergencemissing data mechanismmodel selectionregression

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Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • Missing data presents significant challenges in statistical modeling and analysis.
  • Traditional model selection methods often struggle with incomplete datasets.
  • The Expectation-Maximization (E-M) algorithm is a powerful tool for handling missing data but typically requires a pre-defined model.

Purpose of the Study:

  • To develop a novel algorithm, E-MS, that integrates model selection directly into the E-M algorithm's iterative process.
  • To extend the E-M framework to simultaneously estimate model parameters and select the optimal model.
  • To address the complexities of model selection in the presence of missing data.

Main Methods:

  • Development of the E-MS (Expectation-Maximization for Model Selection) algorithm for finite model classes.
  • Investigation of special cases: E-MS with Generalized Information Criteria (GIC) and E-MS with Adaptive Fence (AF).
  • Theoretical proofs of numerical convergence and consistency in model selection for E-MS with GIC and E-MS with AF.

Main Results:

  • The E-MS algorithm demonstrates numerical convergence and consistent model selection properties.
  • The study analyzes the influence of various missing data mechanisms on model selection outcomes.
  • Extensive simulations confirm the robust finite-sample performance of E-MS compared to existing methods.

Conclusions:

  • The E-MS algorithm provides a robust and consistent framework for model selection with missing data.
  • The methodology is validated through theoretical analysis, simulations, and a practical application in QTL mapping.
  • This approach enhances statistical modeling capabilities for datasets with incomplete information.