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Adaptive Sampling Designs pp 11–26Cite as

Adaptive Cluster Sampling

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Part of the book series: SpringerBriefs in Statistics ((BRIEFSSTATIST))

Abstract

One of the main methods of adaptive sampling is adaptive cluster sampling. As it involves unequal probability of sampling, standard Horvitz-Thompson and Hansen-Hurwitz estimators can be modified to provide unbiased estimates of finite population parameters along with unbiased variance estimators. These estimators are compared with each other and with conventional estimators. Confidence intervals are discussed, including bootstrap and empirical likelihood methods, and a biased estimator that we call Hájek’s estimator is described because of its link with this topic. The chapter closes with some theory about selecting networks without replacement.

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Notes

  1. 1.

    See also http://www.cee.vt.edu/ewr/environmental/teach/smprimer/adaptive/adaptv.html.

  2. 2.

    See http://www.lsc.usgs.gov/AEB/davids/acs/ for single stage, two stage, or stratified sampling.

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Authors and Affiliations

  1.  , 7 Milford Park Place, 0620, North Shore, New Zealand

    George A. F. Seber

  2. Department of Statistics, University of Auckland, Auckland, New Zealand

    George A. F. Seber

  3. Department of Mathematics, Statistics and Physics, Qatar University, Doha, 2713, Qatar

    Mohammad M. Salehi

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  1. George A. F. Seber
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  2. Mohammad M. Salehi
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Correspondence to George A. F. Seber .

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Seber, G.A.F., Salehi, M.M. (2012). Adaptive Cluster Sampling. In: Adaptive Sampling Designs. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33657-7_2

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