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This study introduces non-parametric estimators for weighted extropy, a rarely studied measure. Validation through simulations and real data confirms the usefulness of these new weighted extropy estimation methods.

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Area of Science:

  • Information Theory
  • Statistical Inference

Background:

  • Weighted extropy estimation is an under-researched area in statistical literature.
  • Existing methods for extropy estimation may not be suitable for weighted distributions.

Purpose of the Study:

  • To propose novel non-parametric estimators for weighted extropy.
  • To validate and compare the performance of these new estimators.

Main Methods:

  • Development of non-parametric estimation techniques for weighted extropy.
  • Implementation of simulation studies to assess estimator properties.
  • Application to real-world datasets for practical relevance.

Main Results:

  • The proposed non-parametric estimators for weighted extropy are presented.
  • Simulation results demonstrate the effectiveness and comparative performance of the estimators.
  • Data illustration confirms the practical utility of the developed methods.

Conclusions:

  • The study successfully introduces and validates new non-parametric estimators for weighted extropy.
  • The findings contribute to the limited literature on weighted extropy estimation.
  • The demonstrated usefulness on real data suggests practical applicability in various fields.