scholarly journals Ratio Estimator in Adaptive Cluster Sampling without Replacement of Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Nipaporn Chutiman ◽  
Monchaya Chiangpradit
Keyword(s):  
Auxiliary Variable ◽  
Cluster Sampling ◽  
Ratio Estimator ◽  
Adaptive Cluster Sampling ◽  
Population Total ◽  
Sampling Without Replacement ◽  
Numerical Examples ◽  
Ratio Estimators

In this paper, we study the estimators of the population total in adaptive cluster sampling by using the information of the auxiliary variable. The numerical examples showed that the ratio estimator in adaptive cluster sampling without replacement of networks is more efficient than the ratio estimators in adaptive cluster sampling without replacement of units.

RATIO ESTIMATOR IN ADAPTIVE CLUSTER SAMPLING BASED ON RANKED SET

2016 ◽  
Vol 49 (2) ◽  
pp. 105-116
Author(s):  
Nipaporn Chutiman ◽  
Monchaya Chiangpradit ◽  
Sujitta Suraphee
Keyword(s):  
Cluster Sampling ◽  
Ratio Estimator ◽  
Adaptive Cluster Sampling

scholarly journals Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255256
Author(s):  
Mohammad Salehi ◽  
David R. Smith
Keyword(s):  
Binary Data ◽  
Sampling Design ◽  
A Priori ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Inverse Sampling ◽  
Two Stage ◽  
Population Total ◽  
Unbiased Estimators ◽  
Final Sample

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

Ratio estimators in adaptive cluster sampling

2007 ◽  
Vol 18 (6) ◽  
pp. 607-620 ◽  
Author(s):  
Arthur L. Dryver ◽  
Chang-Tai Chao
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Ratio Estimators

Estimation of rare and clustered population variance in adaptive cluster sampling

2018 ◽  
Vol 48 (21) ◽  
pp. 5387-5400
Author(s):  
Muhammad Nouman Qureshi ◽  
Sadia Khalil ◽  
Chang-Tai Chao ◽  
Muhammad Hanif
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Population Variance

Stratified adaptive cluster sampling

Biometrika ◽  
1991 ◽  
Vol 78 (2) ◽  
pp. 389-397 ◽  
Author(s):  
STEVEN K. THOMPSON
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling
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