Towards the Efficiency of the Ratio Estimator for Population Median in Survey Sampling

Towards the Efficiency of the Ratio Estimator for Population Median in Survey Sampling

Matthew Joshua Iseh

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Akwa Ibom State University

Towards the Efficiency of the Ratio Estimator for Population Median in Survey Sampling

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Abstract

This study leveraged on the fact that researchers in survey sampling sometimes do not take into consideration the tool that will be most appropriate in the measure of location. As a result, users of statistics often go for the mean or total, which has been widely discussed in the finite population sampling literature, unlike the median, which is more complicated to deal with given that it has to do with ordered data. This study suggests an estimator of population median in single and double sampling technique with two auxiliary variables. From the result obtained, it is established that the proposed estimators perform better when the considered variables are from a highly skewed distribution, such as income, expenditure, scores. In addition, it was observed that the proposed estimators were less bias and more efficient than the existing estimators of their class.

References

19 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Matthew Joshua Iseh. 2021. \u201cTowards the Efficiency of the Ratio Estimator for Population Median in Survey Sampling\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 21 (GJSFR Volume 21 Issue F4).

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GJSFR Volume 21 Issue F4

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

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GJSFR-F Classification MSC 2010: 13P25

Submission ReceivedMay 21, 2021
Peer Review Double Blind
Handling Editor
Accepted June 20, 2021
Published July 5, 2021

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v1.2

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October 9, 2021

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