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

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


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.


Ranked set sample (RSS) Extreme ranked set sample (ERSS) Median ranked set sample (MRSS) Simple random sample (SRS) Simulation Naive estimator Regression estimator Ratio estimator Normal data Concomitant variable Varied set size ranked set sampling (VSRSS) Bilirubin Quantiles Bivariate ranked set sampling (BVRSS) Clinical trials 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of BiostatisticsJiann-Ping Hsu College of Public Health, Georgia Southern UniversityStatesboroUSA
  2. 2.Department of BiostatisticsJiann-Ping Hsu College of Public Health, Georgia Southern UniversityStatesboroUSA

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