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General Ranked Set Sampling with Cost Considerations
Author(s) -
Wang YouGan,
Chen Zehua,
Liu Jianbin
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00204.x
Subject(s) - sampling (signal processing) , statistics , set (abstract data type) , computer science , mathematics , econometrics , filter (signal processing) , computer vision , programming language
Summary . Nahhas, Wolfe, and Chen (2002, Biometrics 58, 964–971) considered optimal set size for ranked set sampling (RSS) with fixed operational costs. This framework can be very useful in practice to determine whether RSS is beneficial and to obtain the optimal set size that minimizes the variance of the population estimator for a fixed total cost. In this article, we propose a scheme of general RSS in which more than one observation can be taken from each ranked set. This is shown to be more cost‐effective in some cases when the cost of ranking is not so small. We demonstrate using the example in Nahhas, Wolfe, and Chen (2002, Biometrics 58, 964–971), by taking two or more observations from one set even with the optimal set size from the RSS design can be more beneficial.