Work

Efficient classes of estimators for estimating indeterminate population mean using neutrosophic ranked set sampling

Year: 2025

Type: article

Abstract: Estimating the population mean with accuracy is frequently challenged by uncertainty and inaccurate data in survey sampling. This study presents some efficient classes of estimators for estimating the indeterminate population mean using neutrosophic ranked set sampling (NRSS). The study establishes the bias and mean squared error (MSE) of the suggested estimators and compares their performance with the existing neutrosophic estimators. Analytical comparisons show considerable efficiency benefits over the existing neutrosophic estimators. Simulation research and executions on real-life datasets confirm the accuracy of the proposed neutrosophic estimators when dealing with uncertainty. The findings highlight the potential of proposed NRSS estimators as a powerful tool for population mean estimation by providing new insights into statistical methodologies for uncertain and imprecise situations.

Source: AIMS Mathematics

DOI: 10.3934/math.2025410

Authors: Anoop Kumar, Priya, Abdullah Mohammed Alomair

Institutions: Central University of Haryana, King Faisal University

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: King Faisal University