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On Ranked Set Sampling Variation and Its Applications to Public Health Research

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Innovative Statistical Methods for Public Health Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

The foundation of any statistical inference depends on the collection of required data through some formal mechanism that should be able to capture the distinct characteristics of the population. One of the most common mechanisms to obtain such data is the simple random sample (SRS). In practice, a more structured sampling mechanism, such as stratified sampling, cluster sampling or systematic sampling, may be obtained to achieve a representative sample of the population of interest. A cost effective alternative approach to the aforementioned sampling techniques is the ranked set sampling (RSS). This approach to data collection was first proposed by McIntyre (Aust. J. Agr. Res. 3:385–390, 1952) as a method to improve the precision of estimated pasture yield. In RSS the desired information is obtained from a small fraction of the available units.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Hendricks Hall, Room 1006, Statesboro, GA, 30460, USA

    Hani Samawi

  2. Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Hendricks Hall, Room 1013, Statesboro, GA, 30460, USA

    Robert Vogel

Authors
  1. Hani Samawi
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  2. Robert Vogel
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Correspondence to Hani Samawi or Robert Vogel .

Editor information

Editors and Affiliations

  1. Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Arizona State University, Tempe, Arizona, USA

    Jeffrey Wilson

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Samawi, H., Vogel, R. (2015). On Ranked Set Sampling Variation and Its Applications to Public Health Research. In: Chen, DG., Wilson, J. (eds) Innovative Statistical Methods for Public Health Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18536-1_13

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