Adaptive cluster sampling with a data driven stopping rule

DOI10.1007/s10260-010-0149-5zbMath1332.62207OpenAlexW1988083363MaRDI QIDQ261548

Tonio Di Battista, Stefano Antonio Gattone

Publication date: 24 March 2016

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-010-0149-5

zbMATH Keywords

efficiencyMonte Carlo simulationstopping ruleadaptive cluster sampling

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Sampling theory, sample surveys (62D05) Monte Carlo methods (65C05) Optimal stopping in statistics (62L15) General considerations in statistical decision theory (62C05)

Related Items (2)

k-step adaptive cluster sampling with Horvitz–Thompson estimator ⋮ Adaptive Cluster Sampling in Two-stage Sampling

Cites Work

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• Rate of convergence for asymptotic variance of the Horvitz-Thompson estimator
• 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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