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Improved Estimator Using Auxiliary Information in Adaptive Cluster Sampling with Networks Selected Without Replacement

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Udgivet i:Symmetry vol. 17, no. 3 (2025), p. 375
Hovedforfatter: Chutiman, Nipaporn
Andre forfattere: Nathomthong, Athipakon, Wichitchan, Supawadee, Guayjarernpanishk, Pannarat
Udgivet:
MDPI AG
Fag:
Mean square errors
Variables
Sample size
Network formation
Taylor series
Series expansion
Parameter estimation
Clusters
Resource management
Random sampling
Sampling methods
Adaptive sampling
Efficiency
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Resumen:Adaptive cluster sampling (ACS) is an efficient sampling technique for studying populations where the characteristic of interest is rare or spatially clustered. This method is widely applied in fields such as ecological studies, epidemiology, and resource management. ACS initially selects sampling units using simple random sampling without replacement. However, in some cases, selected networks may overlap, leading to multiple networks being included in the sample. To address this issue, a modified version of ACS was developed to ensure sampling without replacement at the network level, maintaining sampling symmetry and preventing the inclusion of overlapping networks. Despite this adjustment, asymmetry may still occur when network formation is highly irregular. This issue can be mitigated by incorporating auxiliary variables, which help correct distortions in the sampling process. In many situations, auxiliary variables related to the variable of interest can be utilized to enhance the precision of population parameter estimates. This research proposes multiplicative generalization for an estimator with two auxiliary variables using adaptive cluster sampling with networks selected without replacement. The bias and mean square error (MSE) are derived using a Taylor series expansion to determine the optimal conditions for minimizing MSE. A simulation study is conducted to support the theoretical findings. The results show that the proposed estimator under the optimal values of <inline-formula>T1</inline-formula> and <inline-formula>T2</inline-formula> is the most efficient to minimize MSE.
ISSN:2073-8994
DOI:10.3390/sym17030375
Fuente:Engineering Database