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RANKED SET SAMPLING THEORY WITH SELECTIVE PROBABILITY MATRIX 1
Author(s) -
Yanagawa T.,
Shirahata S.
Publication year - 1976
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1976.tb00963.x
Subject(s) - ranking (information retrieval) , estimator , sample (material) , set (abstract data type) , sampling (signal processing) , matrix (chemical analysis) , rough set , sample size determination , computer science , statistics , mathematics , sample mean and sample covariance , algorithm , data mining , artificial intelligence , physics , programming language , filter (signal processing) , computer vision , materials science , composite material , thermodynamics
Banked set sampling theory introduced by Mclntyre (1952) is generalized by introducing a Selective Probability Matrix. The theory is intended to make full use of available information and is most useful in situations ‐where a random sample of small size can be readily ordered by visual inspection or other rough gauging methods, and the exact measurement of the sample is costly in time or effort. In this paper an estimator of the population mean is constructed and several properties of it are investigated to demonstrate the advantages of the theory. The cases of perfect ranking and errors in ranking are discussed in detail and it is made clear that errors in ranking do not destroy the usefulness of the method.