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Robust Model Selection Criteria Based on Pseudodistances.

Aida Toma1,2, Alex Karagrigoriou3, Paschalini Trentou3

  • 1Department of Applied Mathematics, Bucharest University of Economic Studies, 010164 Bucharest, Romania.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces robust model selection criteria using pseudodistances for improved performance, especially with small or contaminated data samples. The new criteria offer a competitive alternative to existing methods in statistical modeling.

Keywords:
Robustnessminimum pseudodistance estimationmodel selection

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Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Model selection is crucial for building accurate statistical and machine learning models.
  • Existing criteria can be sensitive to small or contaminated datasets, leading to suboptimal model choices.

Purpose of the Study:

  • To introduce a novel class of robust model selection criteria.
  • To evaluate the theoretical properties and practical performance of these new criteria.

Main Methods:

  • Utilizing estimators of expected overall discrepancy based on pseudodistances.
  • Applying the minimum pseudodistance principle for criterion definition.
  • Theoretical analysis including asymptotic unbiasedness, robustness, and consistency.
  • Specific criterion development for linear regression models.

Main Results:

  • Theoretical properties such as asymptotic unbiasedness, robustness, consistency, and limit laws are proven.
  • A specific pseudodistance-based criterion for linear regression is proposed.
  • Simulations and real-data applications demonstrate competitive and often superior performance compared to existing criteria, particularly for small and contaminated samples.

Conclusions:

  • The proposed robust model selection criteria offer a valuable alternative to traditional methods.
  • The new criteria exhibit strong performance, especially in challenging data scenarios like small or contaminated samples.