Information content of partially rank-ordered set samples View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-04

AUTHORS

Armin Hatefi, Mohammad Jafari Jozani

ABSTRACT

Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having more flexibility to perform the necessary judgemental ranking process. The PROS sampling has a wide range of applications in different fields ranging from environmental and ecological studies to medical research and it has been shown to be superior over ranked set sampling and simple random sampling for estimating the population mean. We study Fisher information content and uncertainty structure of the PROS samples and compare them with those of simple random sample (SRS) and ranked set sample (RSS) counterparts of the same size from the underlying population. We study uncertainty structure in terms of the Shannon entropy, Rényi entropy and Kullback–Leibler (KL) discrimination measures. More... »

PAGES

117-149

References to SciGraph publications

  • 2004. Ranked Set Sampling, Theory and Applications in NONE
  • 2012-09. Nonparametric mean estimation using partially ordered sets in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 2011-12. Sampling from partially rank-ordered sets in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 1999-06. Best linear unbiased estimators for the simple linear regression model using ranked set sampling in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10182-016-0277-9

    DOI

    http://dx.doi.org/10.1007/s10182-016-0277-9

    DIMENSIONS

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