k-step adaptive cluster sampling with Horvitz–Thompson estimator

DOI10.1142/S1793524518500298zbMath1383.92013OpenAlexW2779155957MaRDI QIDQ4604859

Liyong Fu, Guang-Yu Zhu

Publication date: 6 March 2018

Published in: International Journal of Biomathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s1793524518500298

zbMATH Keywords

unbiased estimatorplant species\(k\)-step adaptive cluster samplingfinalized sample sizesample design improvement

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15)

Cites Work

• Adaptive web sampling in ecology
• Adaptive cluster sampling with a data driven stopping rule
• Adaptive sampling without replacement of clusters
• Adjusted two-stage adaptive cluster sampling
• Adaptive Cluster Sampling
• Inverse Adaptive Cluster Sampling
• Two-Stage Adaptive Cluster Sampling
• Adaptive cluster sampling with networks selected without replacement
• Theory & Methods: Unbiased estimators for restricted adaptive cluster sampling
• Improved Unbiased Estimators in Adaptive Cluster Sampling

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