Open Access

ARTICLE

New Improved Ranked Set Sampling Designs with an Application to Real Data

Amer Ibrahim Al-Omari¹, Ibrahim M. Almanjahie^2,3,*

1 Department of Mathematics, Faculty of Science, Al al-Bayt University, Mafraq, Jordan
2 Department of Mathematics, College of Science, King Khalid University, Abha, 62529, Saudi Arabia
3 Statistical Research and Studies Support Unit, King Khalid University, Abha, 62529, Saudi Arabia

* Corresponding Author: Ibrahim M. Almanjahie. Email:

(This article belongs to the Special Issue: Emerging Computational Intelligence Technologies for Software Engineering: Paradigms, Principles and Applications)

Computers, Materials & Continua 2021, 67(2), 1503-1522. https://doi.org/10.32604/cmc.2021.015047

Received 04 November 2020; Accepted 08 December 2020; Issue published 05 February 2021

Abstract

This article proposes two new Ranked Set Sampling (RSS) designs for estimating the population parameters: Simple Z Ranked Set Sampling (SZRSS) and Generalized Z Ranked Set Sampling (GZRSS). These designs provide unbiased estimators for the mean of symmetric distributions. It is shown that for non-uniform symmetric distributions, the estimators of the mean under the suggested designs are more efficient than those obtained by RSS, Simple Random Sampling (SRS), extreme RSS and truncation based RSS designs. Also, the proposed RSS schemes outperform other RSS schemes and provide more efficient estimates than their competitors under imperfect rankings. The suggested mean estimators under perfect and imperfect rankings are more efficient than the linear regression estimator under SRS. Our proposed RSS designs are also extended to cover the estimation of the population median. Real data is used to examine wthe usefulness and efficiency of our estimators.

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Citations