JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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OPTIMUM FAMILY OF ESTIMATORS IN SIMPLE RANDOM SAMPLING OF FINITE POPULATION MEAN USING TWO AUXILIARY VARIABLES WITH APPLICATIONS IN FISHERIES AND ECONOMICS SECTORS

Authors

  • Ali Algarni

Keywords:

mean, simple random sampling, auxiliary information, mean square error, percentage relative efficiency, numerical comparisons, simulation study, visualization, graphs

DOI:

https://doi.org/10.17654/0973514325011

Abstract

To estimate a finite population mean when the population mean of the auxiliary variable is known, we propose a new family of finite population mean estimators designed for use in simple random sampling situations. We performed first-order approximations of these new estimates and presented mathematical models for their biases and mean square error (MSE). To evaluate the effectiveness of these predictions, we made theoretical and empirical comparisons under different conditions and compared them with traditional predictions. This article demonstrates the advantages and benefits of these new estimators in the forecasting process by providing information on their performance in various simulation configurations.

Received: September 1, 2024
Revised: December 1, 2024
Accepted: December 28, 2024

References

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Published

2025-02-22

Issue

Section

Articles

How to Cite

OPTIMUM FAMILY OF ESTIMATORS IN SIMPLE RANDOM SAMPLING OF FINITE POPULATION MEAN USING TWO AUXILIARY VARIABLES WITH APPLICATIONS IN FISHERIES AND ECONOMICS SECTORS. (2025). JP Journal of Biostatistics, 25(2), 223-242. https://doi.org/10.17654/0973514325011

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