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Adaptive Cluster Sampling

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Advanced Sampling Methods

Abstract

While conducting a sample survey, a number of difficult sampling problems are encountered. One of them is the problem in estimating the population mean/total when it is rare or geographically uneven. If the population of interest is hidden or elusive, then it becomes difficult to identify it for sampling.

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References

  • Cochran, W.G.: Sampling Techniques, 3rd edn. Wiley, New York (1977)

    MATH  Google Scholar 

  • Dryver, A.L., Thompson, S.K.: Improving Unbiased Estimators in Adaptive Cluster Sampling. Technical Report, Department of Statistics, Pennsylvania State University (1998a)

    Google Scholar 

  • Dryver, A.L., Thompson, S.K.: Adaptive Cluster Sampling Without Replacement of Clusters. Technical Report 9, Department of Statistics, Pennsylvania State University (1998b)

    Google Scholar 

  • Elliott, M.R., Little, R.J.A.: Model-based alternatives to trimming survey weights. J. Off. Stat. 16(3), 191–209 (2000)

    Google Scholar 

  • Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics, 2nd edn. Wiley, New York (2002)

    Book  Google Scholar 

  • Thompson, S.K.: Adaptive cluster sampling. J. Am. Stat. Assoc. 85(412), 1050–1058 (1990)

    Article  MathSciNet  Google Scholar 

  • Thompson, S.K., Seber, G.A.F.: Adaptive Sampling. Wiley, New York (1996)

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Correspondence to Raosaheb Latpate .

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Latpate, R., Kshirsagar, J., Kumar Gupta, V., Chandra, G. (2021). Adaptive Cluster Sampling. In: Advanced Sampling Methods. Springer, Singapore. https://doi.org/10.1007/978-981-16-0622-9_10

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