Novel efficient estimators of finite population mean in stratified random sampling with application
Unbiased estimators are valuable when no auxiliary information is available beyond the primary study variables. However, once auxiliary information is accessible, biased estimators with smaller Mean Square Error (MSE) often outperform unbiased estimators that have large variances. We sought to devel...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-03-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2025254 |
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| author | Khazan Sher Muhammad Ameeq Muhammad Muneeb Hassan Basem A. Alkhaleel Sidra Naz Olyan Albalawi |
| author_facet | Khazan Sher Muhammad Ameeq Muhammad Muneeb Hassan Basem A. Alkhaleel Sidra Naz Olyan Albalawi |
| author_sort | Khazan Sher |
| collection | DOAJ |
| description | Unbiased estimators are valuable when no auxiliary information is available beyond the primary study variables. However, once auxiliary information is accessible, biased estimators with smaller Mean Square Error (MSE) often outperform unbiased estimators that have large variances. We sought to develop new estimators that incorporate a single auxiliary variable in stratified random sampling. This study contributes to the field by introducing two distinct families of estimators designed to estimate the finite population mean. We conducted a theoretical evaluation of the estimators' performance by examining bias and MSE derived under first-order approximation. Additionally, we established the theoretical conditions necessary for the proposed estimator families to exhibit superior performance compared with existing alternatives. Empirical and simulation-based studies demonstrated significant improvements in estimators over competing estimators for finite-population parameter estimation. |
| format | Article |
| id | doaj-art-83aee90b0d204db8b171d76dd1b49fa3 |
| institution | OA Journals |
| issn | 2473-6988 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-83aee90b0d204db8b171d76dd1b49fa32025-08-20T02:26:19ZengAIMS PressAIMS Mathematics2473-69882025-03-011035495553110.3934/math.2025254Novel efficient estimators of finite population mean in stratified random sampling with applicationKhazan Sher0Muhammad Ameeq1Muhammad Muneeb Hassan2Basem A. Alkhaleel3Sidra Naz4Olyan Albalawi5Department of Statistics University of Peshawar, PakistanDepartment of Statistics, The Islamia University Bahawalpur, Punjab PakistanDepartment of Statistics, The Islamia University Bahawalpur, Punjab PakistanDepartment of Industrial Engineering, King Saud University, Riyadh 12372, Saudi ArabiaDepartment of Statistics, The Islamia University Bahawalpur, Punjab PakistanDepartment of Statistics, Faculty of Science, University of Tabuk Saudi ArabiaUnbiased estimators are valuable when no auxiliary information is available beyond the primary study variables. However, once auxiliary information is accessible, biased estimators with smaller Mean Square Error (MSE) often outperform unbiased estimators that have large variances. We sought to develop new estimators that incorporate a single auxiliary variable in stratified random sampling. This study contributes to the field by introducing two distinct families of estimators designed to estimate the finite population mean. We conducted a theoretical evaluation of the estimators' performance by examining bias and MSE derived under first-order approximation. Additionally, we established the theoretical conditions necessary for the proposed estimator families to exhibit superior performance compared with existing alternatives. Empirical and simulation-based studies demonstrated significant improvements in estimators over competing estimators for finite-population parameter estimation.https://www.aimspress.com/article/doi/10.3934/math.2025254unbiasednessauxiliarystratified random samplingefficiencymean square error |
| spellingShingle | Khazan Sher Muhammad Ameeq Muhammad Muneeb Hassan Basem A. Alkhaleel Sidra Naz Olyan Albalawi Novel efficient estimators of finite population mean in stratified random sampling with application AIMS Mathematics unbiasedness auxiliary stratified random sampling efficiency mean square error |
| title | Novel efficient estimators of finite population mean in stratified random sampling with application |
| title_full | Novel efficient estimators of finite population mean in stratified random sampling with application |
| title_fullStr | Novel efficient estimators of finite population mean in stratified random sampling with application |
| title_full_unstemmed | Novel efficient estimators of finite population mean in stratified random sampling with application |
| title_short | Novel efficient estimators of finite population mean in stratified random sampling with application |
| title_sort | novel efficient estimators of finite population mean in stratified random sampling with application |
| topic | unbiasedness auxiliary stratified random sampling efficiency mean square error |
| url | https://www.aimspress.com/article/doi/10.3934/math.2025254 |
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