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

Srikant Gupta (), Ahteshamul Haq () and Rahul Varshney ()
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Srikant Gupta: Jaipuria Institute of Management
Ahteshamul Haq: Aligarh Muslim University
Rahul Varshney: Babasaheb Bhimrao Ambedkar University

Annals of Data Science, 2024, vol. 11, issue 2, No 2, 425-444

Abstract: 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.

Keywords: Nonlinear programming problem; Multi-objective programming; Multi-variate stratified sampling; Intuitionistic fuzzy programming (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-022-00410-y

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