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Partial ranked set sampling design
Author(s) -
Haq Abdul,
Brown Jennifer,
Moltchanova Elena,
AlOmari Amer Ibrahim
Publication year - 2013
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2203
Subject(s) - estimator , simple random sample , sampling (signal processing) , statistics , monte carlo method , variance (accounting) , computer science , set (abstract data type) , systematic sampling , sampling design , basis (linear algebra) , data set , population , econometrics , mathematics , demography , filter (signal processing) , sociology , computer vision , programming language , accounting , geometry , business
In many environmental studies, the main focus is on observational economy, that is, to obtain data on the basis of cost‐effective and efficient sampling methods. In this paper, we propose a partial ranked set sampling (PRSS) method for estimation of population mean, median and variance. On the basis of perfect and imperfect rankings, Monte Carlo simulations from symmetric and asymmetric distributions are used to evaluate the effectiveness of the proposed estimators. It is found that the estimators under PRSS are more efficient than the estimators based on simple random sampling. The procedure is illustrated with a case study using a real data set. Copyright © 2013 John Wiley & Sons, Ltd.