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

Author

Listed:
  • Srikant Gupta

    (Jaipuria Institute of Management)

  • Ahteshamul Haq

    (Aligarh Muslim University)

  • Rahul Varshney

    (Babasaheb Bhimrao Ambedkar University)

Abstract

The investigators always have difficulty selecting a sample for the practical use of the stratified random sampling, such that the precision of the finite population under cost constraints is optimized significantly. Identifying stratum boundaries in stratified sample design also includes an essential recurrent challenge in the sampling process. This study presents a realistic way to stratify the population observation based on compromise analysis. The problem is formulated as a deterministic multivariate stratified sampling optimization model with integer variables and is solved by intuitionistic fuzzy programming. Computational studies using two instances demonstrate the optimization of variances inside the strata, therefore considerably reducing accompanying standard errors. Since the suggested model seeks to minimize variances, it can be applied, for example, microeconomic simulation studies, in which an accurate sample is crucial.

Suggested Citation

  • Srikant Gupta & Ahteshamul Haq & Rahul Varshney, 2024. "Problem of Compromise Allocation in Multivariate Stratified Sampling Using Intuitionistic Fuzzy Programming," Annals of Data Science, Springer, vol. 11(2), pages 425-444, April.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:2:d:10.1007_s40745-022-00410-y
    DOI: 10.1007/s40745-022-00410-y
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