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Double extreme-cum-median ranked set sampling.

Muhammad Zubair1, Seyab Yasin1, Afrah Al-Bossly2

  • 1Department of Economics and Statistics, Dr Hasan Murad School of Management (HSM), University of Management and Technology, Lahore, Pakistan.

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|December 19, 2024
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Summary
This summary is machine-generated.

A new sampling method, double extreme-cum-median ranked set sampling, improves data reliability for heterogeneous and non-normal populations. It outperforms existing techniques, especially with perfect ranking on Weibull distributions.

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Area of Science:

  • Statistics
  • Sampling Techniques

Background:

  • Heterogeneity and extreme values pose challenges in statistical sampling.
  • Existing methods like extreme-cum-median and double ranked set sampling address specific issues but not comprehensively.
  • Research on sampling from heterogeneous and non-normal populations remains limited.

Purpose of the Study:

  • To introduce a novel ranked set sampling procedure: double extreme-cum-median ranked set sampling (DEMRSS).
  • To enhance sampling reliability for heterogeneous and non-normal data.
  • To address the identified research gap in statistical sampling methodologies.

Main Methods:

  • Development of the double extreme-cum-median ranked set sampling (DEMRSS) procedure.
  • Conducting a simulation study using symmetric and asymmetric probability distributions.
  • Performing an empirical study with real-life skewed distribution data.

Main Results:

  • The proposed DEMRSS scheme demonstrates superior performance compared to existing methods.
  • Optimal performance was observed with perfect ranking, particularly for the Weibull distribution.
  • Simulation results were corroborated by the empirical study using real-life data.

Conclusions:

  • The DEMRSS technique offers a robust solution for sampling from heterogeneous and non-normal populations.
  • Its flexible ranking options make it adaptable to various data distributions.
  • The method provides more reliable samples, addressing limitations of prior techniques.