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.
Author(s): Carlos N Bouza-Herrera
Series: Advances in Data Mining and Database Management
Publisher: Engineering Science Reference
Year: 2021
Language: English
Pages: 276
City: Hershey
Cover
Title Page
Copyright Page
Book Series
Table of Contents
Detailed Table of Contents
Preface
Acknowledgment
Chapter 1: An Improved Estimation of Parameter of Morgenstern-Type Bivariate Exponential Distribution Using Ranked Set Sampling
Chapter 2: Item Count Technique in Ranked Set Sampling
Chapter 3: On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design
Chapter 4: Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling
Chapter 5: A Study of Gjestvang and Singh Randomized Response Model Using Ranked Set Sampling
Chapter 6: On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design
Chapter 7: Stratified Ranked Set Sampling (SRSS) for Estimating the Population Mean With Ratio-Type Imputation of the Missing Values
Chapter 8: A Review of Bootstrap Methods in Ranked Set Sampling
Chapter 9: Fuzzy-Weighted Ranked Set Sampling Method
Chapter 10: Stratified Ranked Set Sampling With Missing Observations for Estimating the Difference
Compilation of References
Related References
About the Contributors
Index
