Table of Contents
ISRN Probability and Statistics
Volume 2012 (2012), Article ID 568385, 32 pages
http://dx.doi.org/10.5402/2012/568385
Review Article

Ranked Set Sampling: Its Relevance and Impact on Statistical Inference

Department of Statistics, The Ohio State University, Columbus, OH 43210, USA

Received 21 October 2012; Accepted 8 November 2012

Academic Editors: F. Fagnola, S. Lototsky, C. Proppe, and L. Sacerdote

Copyright © 2012 Douglas A. Wolfe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ranked set sampling (RSS) is an approach to data collection and analysis that continues to stimulate substantial methodological research. It has spawned a number of related methodologies that are active research arenas as well, and it is finally beginning to find its way into significant applications beyond its initial agricultural-based birth in the seminal paper by McIntyre (1952). In this paper, we provide an introduction to the basic concepts underlying ranked set sampling, in general, with specific illustrations from the one- and two-sample settings. Emphasis is on the breadth of the ranked set sampling approach, with targeted discussion of the many options available to the researcher within the RSS paradigm. The paper also provides a thorough bibliography of the current state of the field and introduces the reader to some of the most promising new methodological extensions of the RSS approach to statistical data analysis.