Document Type : Research Paper

Authors

• Abhishek Singh ¹
• Hemant Kulkarni ²
• Florentin Smarandache ³
• Gajendra K. Vishwakarma ⁴

¹ Department of Mathematics and Statistics, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India

² Department of Mathematics & Statistics, Dr. Vishwanath Karad MIT World Peace University, Pune-411038

³ Mathematics, Physical and Natural Science Division, University of New Mexico, Gallup, 705 Gurley Ave., Gallup, NM 87301, USA

⁴ Department of Mathematics and Computing, Indian Institute of Technology ISM Dhanbad, 826004, Jharkhand, India

Abstract

In this article, we introduce a novel approach by presenting separate ratio and regression estimators in the context of neutrosophic stratified sampling for the very first time, incorporating auxiliary variables. We have conducted a thorough analysis to estimate these newly proposed estimators' bias and mean square error (MSE) up to the first-order approximation. Theoretically using efficiency comparison criteria, our findings demonstrate the superior performance of these estimators compared to traditional unbiased estimators. Also, numerically based on real-life and artificial data, we have shown the supremacy of the neutrosophic stratified sampling over neutrosophic simple random sampling along with the supremacy of our proposed neutrosophic separate stratified estimators over neutrosophic stratified unbiased estimator. Moreover, our research highlights the enhanced reliability of neutrosophic stratified estimators when contrasted with classical stratified estimators.

Keywords

• Neutrosophic variables
• Neutrosophic stratified sampling
• Regression and ratio estimator
• Monte-Carlo simulation
• Mean square error

Main Subjects

• Neutrosophic sets and their variants