Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Pareto Sampling versus Sampford and Conditional Poisson Sampling

Lennart Bondesson, Imbi Traat and Anders Lundqvist
Scandinavian Journal of Statistics
Vol. 33, No. 4 (Dec., 2006), pp. 699-720
Stable URL: http://www.jstor.org/stable/4616953
Page Count: 22
  • Read Online (Free)
  • Download ($12.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Pareto Sampling versus Sampford and Conditional Poisson Sampling
Preview not available

Abstract

Pareto sampling was introduced by Rosén in the late 1990s. It is a simple method to get a fixed size πps sample though with inclusion probabilities only approximately as desired. Sampford sampling, introduced by Sampford in 1967, gives the desired inclusion probabilities but it may take time to generate a sample. Using probability functions and Laplace approximations, we show that from a probabilistic point of view these two designs are very close to each other and asymptotically identical. A Sampford sample can rapidly be generated in all situations by letting a Pareto sample pass an acceptance-rejection filter. A new very efficient method to generate conditional Poisson (CP) samples appears as a byproduct. Further, it is shown how the inclusion probabilities of all orders for the Pareto design can be calculated from those of the CP design. A new explicit very accurate approximation of the second-order inclusion probabilities, valid for several designs, is presented and applied to get single sum type variance estimates of the Horvitz-Thompson estimator.

Page Thumbnails

  • Thumbnail: Page 
[699]
    [699]
  • Thumbnail: Page 
700
    700
  • Thumbnail: Page 
701
    701
  • Thumbnail: Page 
702
    702
  • Thumbnail: Page 
703
    703
  • Thumbnail: Page 
704
    704
  • Thumbnail: Page 
705
    705
  • Thumbnail: Page 
706
    706
  • Thumbnail: Page 
707
    707
  • Thumbnail: Page 
708
    708
  • Thumbnail: Page 
709
    709
  • Thumbnail: Page 
710
    710
  • Thumbnail: Page 
711
    711
  • Thumbnail: Page 
712
    712
  • Thumbnail: Page 
713
    713
  • Thumbnail: Page 
714
    714
  • Thumbnail: Page 
715
    715
  • Thumbnail: Page 
716
    716
  • Thumbnail: Page 
717
    717
  • Thumbnail: Page 
718
    718
  • Thumbnail: Page 
719
    719
  • Thumbnail: Page 
720
    720