Robust extreme ranked set sampling

Title: Robust extreme ranked set sampling
Creator: Al-Nasser, Amjad D.; Bani-Mustafa, Ahmed
Date: 2009
Type: Text; Journal article
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/64602
Identifier: vital:2270
Identifier: https://doi.org/10.1080/00949650701683084
Identifier: ISSN:0094-9655
Abstract: In this paper, a robust extreme ranked set sampling (RERSS) procedure for estimating the population mean is introduced. It is shown that the proposed method gives an unbiased estimator with smaller variance, provided the underlying distribution is symmetric. However, for asymmetric distributions a weighted mean is given, where the optimal weights are computed by using Shannon's entropy. The performance of the population mean estimator is discussed along with its properties. Monte Carlo simulations are used to demonstrate the performance of the RERSS estimator relative to the simple random sample (SRS), ranked set sampling (RSS) and extreme ranked set sampling (ERSS) estimators. The results indicate that the proposed estimator is more efficient than the estimators based on the traditional sampling methods. [ABSTRACT FROM AUTHOR]
Relation: Journal of Statistical Computation & Simulation Vol. 79, no. 7 (2009), p. 859-867
Rights: Copyright Taylor & Francis
Rights: This metadata is freely available under a CCO license
Subject: Extreme ranked set sampling; Outliers; Shannon's entropy
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