Stratified Folded Ranked Set Sampling with Perfect Ranking

Chainarong  Peanpailoon; Nassamon  Bootwisas; Noppakun  Thongmual

Authors

Chainarong Peanpailoon Department of Organization Curriculum and Teaching Program (Mathematics), Faculty of Education, Sakonnakhon Rajabhat University, Sakonnakhon, Thailand
Nassamon Bootwisas Department of Computational, Faculty of Science and Technology, Rajamangala University of Technology Suvarnabhumi, Phra Nakhon Si Ayutthaya, Thailand
Noppakun Thongmual Department of Sciences and Mathematics, Faculty of Technology and Healthy of Science, Kalasin University, Kalasin, Thailand

Keywords:

Simple random sampling, stratified simple random sampling, stratified ranked set sampling, stratified folded ranked set sampling

Abstract

This study introduces the Stratified Folded Ranked Set Sampling with Perfect Ranking (SFRSS) method, a novel approach to enhance population mean estimation. SFRSS integrates stratification and folding techniques within the framework of Ranked Set Sampling (RSS), addressing inefficiencies in conventional methods, particularly under symmetric distribution assumptions. The unbiasedness of the SFRSS estimator is established, and its variance is shown to be lower compared to Simple Random Sampling (SRS), Stratified Simple Random Sampling (SSRS), and Stratified Ranked Set Sampling (SRSS). Simulation studies conducted across Uniform, Normal, and Student-t distributions demonstrate the superior efficiency of SFRSS, particularly for heavy-tailed distributions, where ranking and folding significantly reduce variance. The findings highlight SFRSS as a robust alternative for stratified sampling, providing practical benefits in scenarios where population symmetry and stratification play a key role.

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