Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using...

Area of Science:

• Statistics
• Survey Methodology
• Data Science

Background:

• Classical statistics struggle with uncertain data in complex surveys.
• Neutrosophic statistics handle indeterminate and fuzzy information using neutrosophic numbers.
• Existing methods lack robust handling of uncertainty in survey sampling allocation.

Purpose of the Study:

• To address the compromise optimum allocation problem in multi-character stratified random sampling with uncertain per unit measurement costs.
• To develop novel methods for estimating population means of neutrosophic study variables.
• To model fuzzy uncertainty in stratum per unit measurement costs using an intuitionistic fuzzy cost function.

Main Methods:

• Formulation of the compromise optimum allocation as a multi-objective intuitionistic fuzzy optimization problem.
• Application of neutrosophic fuzzy programming and intuitionistic fuzzy programming for solution methodology.
• Utilizing Python for statistical analysis and GAMS for numerical optimization.

Main Results:

• Proposed methods yield more precise estimates compared to existing approaches.
• The suggested techniques demonstrate efficient utilization of survey resources.
• Numerical study on atmospheric variables validates the practical applicability and effectiveness.

Conclusions:

• Neutrosophic statistics offer a powerful framework for handling uncertainty in complex survey sampling.
• The developed optimization approaches provide superior solutions for allocation problems.
• This research enhances the precision and efficiency of statistical inference in uncertain environments.