Estimation of population mean using ranked set sampling in the presence of measurement errors
Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to asse...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-07-01
|
| Series: | Kuwait Journal of Science |
| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410824000610 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849688279492853760 |
|---|---|
| author | Ahmadini A.A.H. Singh R. Raghav Y.S. |
| author_facet | Ahmadini A.A.H. Singh R. Raghav Y.S. |
| author_sort | Ahmadini A.A.H. |
| collection | DOAJ |
| description | Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors. The expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation, revealing their superior performance compared to the other examined estimators. The efficacy of the suggested estimators in handling measurement errors was demonstrated through numerical illustration and simulation study investigations. The recommended estimators are further compared to the existing ones using the percentage relative efficiency and mean squared error, and the impact of measurement errors on the results is highlighted through the percentage computation of measurement errors. The innovative estimators suggested were formulated by judiciously incorporating ratio, exponential, and log estimators. Numerical examples involving expenditure and income, as well as simulated data generated from a normal population using R software, affirm the superior performance of the proposed estimators over existing ones such as the usual mean estimator and those proposed by Vishwakarma and Singh (2022), as evidenced by the higher percent relative efficiency and lower mean squared error. The evaluation of the percentage contribution of measurement error values confirms the impact of measurement errors on the properties of the estimators. © 2024 The Author(s) |
| format | Article |
| id | doaj-art-23c9f84098a24975bcee71b1d11f3721 |
| institution | DOAJ |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-23c9f84098a24975bcee71b1d11f37212025-08-20T03:22:03ZengElsevierKuwait Journal of Science2307-41082307-41162024-07-0151310023610.1016/j.kjs.2024.100236Estimation of population mean using ranked set sampling in the presence of measurement errorsAhmadini A.A.H.Singh R.Raghav Y.S.Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors. The expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation, revealing their superior performance compared to the other examined estimators. The efficacy of the suggested estimators in handling measurement errors was demonstrated through numerical illustration and simulation study investigations. The recommended estimators are further compared to the existing ones using the percentage relative efficiency and mean squared error, and the impact of measurement errors on the results is highlighted through the percentage computation of measurement errors. The innovative estimators suggested were formulated by judiciously incorporating ratio, exponential, and log estimators. Numerical examples involving expenditure and income, as well as simulated data generated from a normal population using R software, affirm the superior performance of the proposed estimators over existing ones such as the usual mean estimator and those proposed by Vishwakarma and Singh (2022), as evidenced by the higher percent relative efficiency and lower mean squared error. The evaluation of the percentage contribution of measurement error values confirms the impact of measurement errors on the properties of the estimators. © 2024 The Author(s)https://www.sciencedirect.com/science/article/pii/S2307410824000610auxiliary variablebiasmean square errormeasurement errorranked set samplingstudy variable |
| spellingShingle | Ahmadini A.A.H. Singh R. Raghav Y.S. Estimation of population mean using ranked set sampling in the presence of measurement errors Kuwait Journal of Science auxiliary variable bias mean square error measurement error ranked set sampling study variable |
| title | Estimation of population mean using ranked set sampling in the presence of measurement errors |
| title_full | Estimation of population mean using ranked set sampling in the presence of measurement errors |
| title_fullStr | Estimation of population mean using ranked set sampling in the presence of measurement errors |
| title_full_unstemmed | Estimation of population mean using ranked set sampling in the presence of measurement errors |
| title_short | Estimation of population mean using ranked set sampling in the presence of measurement errors |
| title_sort | estimation of population mean using ranked set sampling in the presence of measurement errors |
| topic | auxiliary variable bias mean square error measurement error ranked set sampling study variable |
| url | https://www.sciencedirect.com/science/article/pii/S2307410824000610 |
| work_keys_str_mv | AT ahmadiniaah estimationofpopulationmeanusingrankedsetsamplinginthepresenceofmeasurementerrors AT singhr estimationofpopulationmeanusingrankedsetsamplinginthepresenceofmeasurementerrors AT raghavys estimationofpopulationmeanusingrankedsetsamplinginthepresenceofmeasurementerrors |