A prediction approach in adaptive sampling

Abstract

Adaptive cluster sampling (ACS) due to Thompson (J Am Stat Assoc 85(412):1050–1059, 1990) is a tool to survey rare and hidden elements in a population as an improvement over traditional survey procedures. In ACS, if an observed sampled unit satisfies the given criterion of rarity, its neighboring units are added to the sample and this is continued until one is detected with no rarity. Chaudhuri (Calcutta Stat Assoc Bull 50(3–4):238–253, 2000) extended the above to unequal probability sampling as Adaptive sampling. In practice, often network sizes turn out too big demanding high cost and time. So, Chaudhuri et al. (J Stat Plan Inference 121: 175–189, 2004) gave a subsampling technique to restrict the sample size in Adaptive Sampling. Towards this end, Chaudhuri et al. (J Stat Plan Inference 134: 254–267, 2005) developed a sample size restriction technique. But in Adaptive sampling, capturing neighboring rare units turn out difficult because of various hazards. We propose to try Royall’s (Biometrika 57: 377–389, 1970) prediction approach here to model features of uncaptured network units. We employ Brewer’s (J Am Stat Assoc 74 (368): 911–915, 1979) model-assisted approach to derive a predictor with asymptotic design unbiasedness based on unequal probability samples and examine its efficacy by simulations.

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Acknowledgements

The authors are grateful to the editor and the referees for their valuable comments and suggestions.

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Correspondence to Sanghamitra Pal.

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Pal, S., Patra, D. A prediction approach in adaptive sampling. METRON (2021). https://doi.org/10.1007/s40300-020-00195-1

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Keywords

  • Adaptive sampling
  • Asymptotic
  • Model assisted approach
  • Prediction approach
  • Unequal probability

Mathematics Subject Classification

  • 62D05