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Basic Ideas

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Adaptive Sampling Designs

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

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Abstract

In this chapter we consider the problem of estimating such quantities as the number of objects, the total biomass, or total ground cover in a finite population from a sample. Various traditional methods of sampling such as sampling with or without replacement, inverse sampling, and unequal probability sampling are often inadequate when the population is rare but clustered. We briefly introduce the idea of adaptive sampling that includes a variety of so-called adaptive methods. For example, adaptive cluster sampling allows us to sample the rest of a cluster when one is located. We can also have adaptive allocation in stratified sampling where the initial observations in the strata determine the allocation of future observations.

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Notes

  1. 1.

    We use the traditional divisor \(N_h-1\) instead of \(N_h\) as it simplifies expressions.

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Author information

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

Authors
  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). Basic Ideas. In: Adaptive Sampling Designs. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33657-7_1

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