Partial ranked set sampling design

Authors


Correspondence to: Abdul Haq, Department ofMathematics and Statistics, University of Canterbury, Christchurch, New Zealand. E-mail: aaabdulhaq@yahoo.com

Abstract

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.

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