Enhanced Ratio-Type Estimators in Adaptive Cluster Sampling Using Jackknife Method

Supawadee Wichitchan, Athipakon Nathomthong, Pannarat Guayjarernpanishk and Nipaporn Chutiman ()
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Supawadee Wichitchan: Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, Thailand
Athipakon Nathomthong: Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, Thailand
Pannarat Guayjarernpanishk: Faculty of Interdisciplinary Studies, Nong Khai Campus, Khon Kaen University, Nong Khai 43000, Thailand
Nipaporn Chutiman: Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, Thailand

Mathematics, 2025, vol. 13, issue 12, 1-15

Abstract: 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.

Keywords: adaptive cluster sampling; auxiliary information; ratio estimator; jackknife method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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