Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method
Adaptive cluster sampling is a methodology designed for data collection in contexts where the population is rare and spatially clustered. This approach has been effectively applied in various disciplines, including epidemiology and resource management. The present study introduces novel estimators t...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/12/2020 |
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| author | Supawadee Wichitchan Athipakon Nathomthong Pannarat Guayjarernpanishk Nipaporn Chutiman |
| author_facet | Supawadee Wichitchan Athipakon Nathomthong Pannarat Guayjarernpanishk Nipaporn Chutiman |
| author_sort | Supawadee Wichitchan |
| collection | DOAJ |
| description | Adaptive cluster sampling is a methodology designed for data collection in contexts where the population is rare and spatially clustered. This approach has been effectively applied in various disciplines, including epidemiology and resource management. The present study introduces novel estimators that incorporate auxiliary variable information to improve estimation efficiency. These estimators were developed using the jackknife resampling technique to improve the performance of ratio-type estimators. Theoretical properties, including bias and mean square error (MSE), were derived, and a simulation study was conducted to validate the theoretical findings. The results demonstrated that the proposed estimators consistently outperformed conventional estimators that do not utilize auxiliary variables across all network sample sizes. Furthermore, in several scenarios, the proposed estimators also exhibited superior efficiency to existing ratio estimators that do incorporate auxiliary information. |
| format | Article |
| id | doaj-art-96b4126c5bfb406fa8d40715f72a2fb1 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-96b4126c5bfb406fa8d40715f72a2fb12025-08-20T03:27:18ZengMDPI AGMathematics2227-73902025-06-011312202010.3390/math13122020Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife MethodSupawadee Wichitchan0Athipakon Nathomthong1Pannarat Guayjarernpanishk2Nipaporn Chutiman3Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, ThailandFaculty of Interdisciplinary Studies, Nong Khai Campus, Khon Kaen University, Nong Khai 43000, ThailandDepartment of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, ThailandAdaptive cluster sampling is a methodology designed for data collection in contexts where the population is rare and spatially clustered. This approach has been effectively applied in various disciplines, including epidemiology and resource management. The present study introduces novel estimators that incorporate auxiliary variable information to improve estimation efficiency. These estimators were developed using the jackknife resampling technique to improve the performance of ratio-type estimators. Theoretical properties, including bias and mean square error (MSE), were derived, and a simulation study was conducted to validate the theoretical findings. The results demonstrated that the proposed estimators consistently outperformed conventional estimators that do not utilize auxiliary variables across all network sample sizes. Furthermore, in several scenarios, the proposed estimators also exhibited superior efficiency to existing ratio estimators that do incorporate auxiliary information.https://www.mdpi.com/2227-7390/13/12/2020adaptive cluster samplingauxiliary informationratio estimatorjackknife method |
| spellingShingle | Supawadee Wichitchan Athipakon Nathomthong Pannarat Guayjarernpanishk Nipaporn Chutiman Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method Mathematics adaptive cluster sampling auxiliary information ratio estimator jackknife method |
| title | Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method |
| title_full | Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method |
| title_fullStr | Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method |
| title_full_unstemmed | Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method |
| title_short | Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method |
| title_sort | enhanced ratio type estimators in adaptive cluster sampling using jackknife method |
| topic | adaptive cluster sampling auxiliary information ratio estimator jackknife method |
| url | https://www.mdpi.com/2227-7390/13/12/2020 |
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