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Neutrosophic Mean Estimation of Sensitive and Non-Sensitive Variables with Robust Hartley–Ross-Type Estimators

Authors :
Abdullah Mohammed Alomair
Usman Shahzad
Source :
Axioms, Vol 12, Iss 6, p 578 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Under classical statistics, research typically relies on precise data to estimate the population mean when auxiliary information is available. Outliers can pose a significant challenge in this process. The ultimate goal is to determine the most accurate estimates of the population mean while minimizing variance. Neutrosophic statistics is a generalization of classical statistics that deals with imprecise, uncertain data. Our research introduces the neutrosophic Hartley–Ross-type ratio estimators for estimating the population mean of neutrosophic data, even in the presence of outliers. We also incorporate neutrosophic versions of several robust regression methods, including LAD, Huber-M, Hampel-M, and Tukey-M. Our approach assumes that the study variable is both non-sensitive and sensitive, meaning that it can cause discomfort to participants during personal interviews, and measurement errors can occur due to dishonest responses. To address potential measurement errors, we propose the use of neutrosophic scrambling response models. Our proposed neutrosophic robust estimators are more effective than existing classical estimators, as confirmed by a computer-based numerical study using real data and simulation.

Details

Language :
English
ISSN :
20751680
Volume :
12
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Axioms
Publication Type :
Academic Journal
Accession number :
edsdoj.fa6716f0a27343e2b6c5d718505602a8
Document Type :
article
Full Text :
https://doi.org/10.3390/axioms12060578