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Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function

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Abstract

In this article, we consider the memory type product estimator to estimate the population mean of the study variable in stratified random sampling. The suggested estimators’ bias and mean square error (MSE) expressions for the first order of approximation are derived. The proposed estimator is compared with the competing estimators under consideration and the efficiencies’ conditions are obtained. Further, we obtain the optimum allocation of samples in different strata along with MSEs. To achieve the solution of the problem, we frame our problem as AINLPP and solve through genetic programming technique. To show the efficiency of the estimator, we conduct simulation study. An empirical analysis based on real-life data is also offered to support the findings of the simulation study. The most efficient estimator is recommended for different practical applications. The suggested estimator is proven to be the best among the competing estimators.

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Acknowledgements

The authors are very much thankful to the editor in chief Prof. Umapada Pal, Section Editor Prof. Swagatam Das and learned referee for their critical reviews, which improved the manuscript.

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No funding received to carry this research work.

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Contributions

SKY planned the study, AP performed the study, and RV prepared the manuscript. GKV wrote the methodological details to finalize the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Gajendra K. Vishwakarma.

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Yadav, S.K., Vishwakarma, G.K., Varshney, R. et al. Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function. SN COMPUT. SCI. 4, 235 (2023). https://doi.org/10.1007/s42979-023-01673-9

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  • DOI: https://doi.org/10.1007/s42979-023-01673-9

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