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Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator.

Elena Castilla1, Nirian Martín2, Leandro Pardo1

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

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces robust Wald-type test statistics using a novel composite minimum density power divergence estimator. These new statistics offer improved reliability for hypothesis testing in statistical analysis.

Keywords:
Wald test statisticWald-type test statisticscomposite likelihoodcomposite minimum density power divergence estimatormaximum composite likelihood estimator

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Area of Science:

  • Statistical inference
  • Hypothesis testing
  • Robust statistics

Background:

  • Composite likelihood methods are widely used in statistical modeling.
  • Traditional Wald tests can be sensitive to model misspecification.
  • Maximum likelihood estimators may lack robustness in certain scenarios.

Purpose of the Study:

  • To develop a robust version of the Wald test statistic for composite likelihood.
  • To introduce and study the properties of the composite minimum density power divergence estimator.
  • To evaluate the robustness of the proposed Wald-type test statistics through simulations.

Main Methods:

  • Utilizing the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator.
  • Developing a new family of test statistics termed Wald-type test statistics.
  • Conducting simulation studies to assess the robustness of the proposed methods for simple and composite null hypotheses.

Main Results:

  • The proposed Wald-type test statistics demonstrate robustness.
  • The composite minimum density power divergence estimator exhibits desirable asymptotic properties.
  • Simulation results confirm the improved performance over traditional methods under misspecification.

Conclusions:

  • The novel Wald-type test statistics offer a robust alternative for hypothesis testing with composite likelihoods.
  • The composite minimum density power divergence estimator is a valuable tool for robust statistical inference.
  • This research enhances the reliability of statistical tests in practical applications.