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<p>This article presents nonparametric estimators for residual extropy under length-biased sampling. It validates the consistency and asymptotic normality of the proposed estimators under suitable regularity conditions for large sample sizes. The performance of the estimators is evaluated using simulated observations and compared based on root mean squared errors across different sample sizes. Furthermore, a confidence interval for the nonparametric kernel estimator is suggested and its performance is evaluated through simulation studies. Finally, a real data application is conducted to demonstrate the usefulness of the estimators.</p>

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Nonparametric estimation of residual extropy function under length-biased sampling

  • 期刊论文

  • 发表日期：

    2025年4月18日

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  • Vaishnavi Pavithradas,
  • Rajesh G.,
  • Richu Rajesh

摘要

This article presents nonparametric estimators for residual extropy under length-biased sampling. It validates the consistency and asymptotic normality of the proposed estimators under suitable regularity conditions for large sample sizes. The performance of the estimators is evaluated using simulated observations and compared based on root mean squared errors across different sample sizes. Furthermore, a confidence interval for the nonparametric kernel estimator is suggested and its performance is evaluated through simulation studies. Finally, a real data application is conducted to demonstrate the usefulness of the estimators.