Problem of Compromise Allocation in Multivariate Stratified Sampling Using Intuitionistic Fuzzy Programming

The article delves into the challenge of compromise allocation in multivariate stratified sampling, focusing on the application of intuitionistic fuzzy programming to optimize sample sizes across various strata. It begins by introducing the concept of data sampling and its importance in statistical analysis, highlighting the need for efficient sampling methods to ensure reliable results. The authors then present a detailed methodology for using intuitionistic fuzzy programming to address the multi-objective optimization problem in multivariate sampling, providing clear definitions and mathematical formulations. The article includes numerical examples to demonstrate the practical application of the proposed method, showcasing how intuitionistic fuzzy programming can be used to achieve a compromise allocation that minimizes variance and improves the accuracy of population estimates. The conclusion emphasizes the importance of considering measurement uncertainty and the need for effective sampling strategies in multivariate studies. Overall, the article offers valuable insights and practical solutions for data scientists and statisticians working on complex sampling problems.