Ranked Set Sampling Models and Methods

Ranked Set Sampling Models and Methods
Author :
Publisher : IGI Global
Total Pages : 276
Release :
ISBN-13 : 9781799875581
ISBN-10 : 179987558X
Rating : 4/5 (8X Downloads)

Book Synopsis Ranked Set Sampling Models and Methods by : Bouza-Herrera, Carlos N.

Download or read book Ranked Set Sampling Models and Methods written by Bouza-Herrera, Carlos N. and published by IGI Global. This book was released on 2021-08-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: When it comes to data collection and analysis, ranked set sampling (RSS) continues to increasingly be the focus of methodological research. This type of sampling is an alternative to simple random sampling and can offer substantial improvements in precision and efficient estimation. There are different methods within RSS that can be further explored and discussed. On top of being efficient, RSS is cost-efficient and can be used in situations where sample units are difficult to obtain. With new results in modeling and applications, and a growing importance in theory and practice, it is essential for modeling to be further explored and developed through research. Ranked Set Sampling Models and Methods presents an innovative look at modeling survey sampling research and new models of RSS along with the future potentials of it. The book provides a panoramic view of the state of the art of RSS by presenting some previously known and new models. The chapters illustrate how the modeling is to be developed and how they improve the efficiency of the inferences. The chapters highlight topics such as bootstrap methods, fuzzy weight ranked set sampling method, item count technique, stratified ranked set sampling, and more. This book is essential for statisticians, social and natural science scientists, physicians and all the persons involved with the use of sampling theory in their research along with practitioners, researchers, academicians, and students interested in the latest models and methods for ranked set sampling.


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