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Model Selection in a Composite Likelihood Framework Based on Density Power Divergence.

Elena Castilla1, Nirian Martín2, Leandro Pardo1

  • 1Interdisciplinary Mathematics Institute and Department of Statistics and O.R. I, Complutense University of Madrid, 28040 Madrid, Spain.

Entropy (Basel, Switzerland)
|December 8, 2020
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Summary
This summary is machine-generated.

This study introduces a new model selection criterion using density power divergence for composite likelihood models. The criterion demonstrates robustness, validated through simulations and numerical examples.

Keywords:
composite likelihoodcomposite minimum density power divergence estimatorsmodel selection

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Area of Science:

  • Statistics
  • Statistical modeling
  • Model selection

Background:

  • Composite likelihood methods are widely used for complex statistical models.
  • Density power divergence (DPD) offers a flexible class of divergence measures.
  • Model selection is crucial for identifying appropriate statistical models.

Purpose of the Study:

  • To propose a novel model selection criterion within the composite likelihood framework.
  • To utilize density power divergence measures and minimum DPD estimators.
  • To investigate the properties of the proposed criterion, particularly its robustness.

Main Methods:

  • Development of a model selection criterion based on density power divergence.
  • Theoretical establishment of asymptotic properties for the criterion.
  • Empirical validation using simulation studies.
  • Application to real-world data through numerical examples.

Main Results:

  • The proposed model selection criterion is defined and its theoretical properties are derived.
  • Simulation studies indicate the criterion's effectiveness in selecting correct models.
  • Numerical examples highlight the robustness of the criterion against model misspecification.
  • The tuning parameter alpha influences the criterion's performance.

Conclusions:

  • The presented model selection criterion offers a robust approach for composite likelihood models.
  • The criterion's performance is supported by both theoretical and empirical evidence.
  • This work contributes to the advancement of statistical model selection techniques.