Vanishing auxiliary variables in PPS sampling - with applications in microscopy

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Abstract

Recently, non-uniform sampling has been suggested in microscopy to increase efficiency. More precisely, sampling proportional to size (PPS) has been introduced where the probability of sampling a unit in the population is proportional to the value of an auxiliary variable. Unfortunately, vanishing auxiliary variables are a common phenomenon in microscopy and, accordingly, part of the population is not accessible, using PPS sampling. We propose a modification of the design, for which an optimal solution can be found, using a model assisted approach. The optimal design has independent interest in sampling theory. We verify robustness of the new approach by numerical results, and we use real data to illustrate the applicability.
Original languageEnglish
PublisherCentre for Stochastic Geometry and Advanced Bioimaging, Aarhus University
Number of pages20
Publication statusPublished - Feb 2014
SeriesCSGB Research Reports
Number01
Volume2014

Keywords

  • microscopy
  • model assisted sampling
  • optimal allocation
  • proportional regression models
  • systematic PPS sampling
  • vanishing auxiliary variables

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