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.
