Nonparametric mean estimation using partially ordered sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-09

AUTHORS

Jesse Frey

ABSTRACT

In ranked-set sampling (RSS), the ranker must give a complete ranking of the units in each set. In this paper, we consider a modification of RSS that allows the ranker to declare ties. Our sampling method is simply to break the ties at random so that we obtain a standard ranked-set sample, but also to record the tie structure for use in estimation. We propose several different nonparametric mean estimators that incorporate the tie information, and we show that the best of these estimators is substantially more efficient than estimators that ignore the ties. As part of our comparison of estimators, we develop new results about models for ties in rankings. We also show that there are settings where, to achieve more efficient estimation, ties should be declared not just when the ranker is actually unsure about how units rank, but also when the ranker is sure about the ranking, but believes that the units are close. More... »

PAGES

309-326

References to SciGraph publications

  • 1995-12. Editorial: ranked set sampling in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 2003-09. Ranked set sampling based on binary water quality data with covariates in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2011-12. Sampling from partially rank-ordered sets in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10651-012-0188-1

    DOI

    http://dx.doi.org/10.1007/s10651-012-0188-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1038751357


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