Ranked Set Sampling Models and Methods
- Length: 276 pages
- Edition: 1
- Language: English
- Publisher: Engineering Science Reference
- Publication Date: 2021-10-26
- ISBN-10: 1799875563
- ISBN-13: 9781799875567
- Sales Rank: #0 (See Top 100 Books)
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.
Cover Title Page Copyright Page Book Series Mission Coverage Preface REFERENCES Acknowledgment Chapter 1: An Improved Estimation of Parameter of Morgenstern-Type Bivariate Exponential Distribution Using Ranked Set Sampling ABSTRACT 1.1. INTRODUCTION 1.2. BACKGROUND 1.3. MAIN FOCUS OF THE CHAPTER 1.4. SOLUTIONS AND RECOMMENDATIONS 1.5. DISCUSSION 1.6. CONCLUSION ACKNOWLEDGMENT REFERENCES Chapter 2: Item Count Technique in Ranked Set Sampling ABSTRACT INTRODUCTION BACKGROUND SIMULATION STUDY CONCLUSION ACKNOWLEDGMENT REFERENCES Chapter 3: On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design ABSTRACT 1. INTRODUCTION 2. EXTENDING SRSWR ESTIMATORS TO STRATIFICATION 3. RSS EXTENSION OF STRATIFIED SRSWR ESTIMATORS 4. NUMERICAL STUDIES 5. CONCLUSION ACKNOWLEDGMENT REFERENCES Chapter 4: Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling ABSTRACT 1. INTRODUCTION 2. SSRS USING SCRAMBLED AUXILIARY VARIABLES 3. A NUMERICAL STUDY 4. CONCLUSION ACKNOWLEDGMENT REFERENCES Chapter 5: A Study of Gjestvang and Singh Randomized Response Model Using Ranked Set Sampling ABSTRACT 1. INTRODUCTION 2. PROPOSED ESTIMATOR 3. SIMULATION STUDY 4. APPLICATIONS: REAL DATA SETS CONCLUSION ACKNOWLEDGMENT REFERENCES APPENDIX-A Chapter 6: On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design ABSTRACT 1. INTRODUCTION 2. RANKED SET SAMPLING 3. EMPIRICAL EVIDENCES: A SIMULATION 4 APPLICATIONS BASED ON REAL DATA ACKNOWLEDGMENT REFERENCES APPENDIX Chapter 7: Stratified Ranked Set Sampling (SRSS) for Estimating the Population Mean With Ratio-Type Imputation of the Missing Values ABSTRACT 1. INTRODUCTION 2. SOME IDEAS ON IMPUTATION 3. STRATIFIED RATIO-TYPE IMPUTATION UNDER SIMPLE RANDOM SAMPLING WITH REPLACEMENT (SSRSWR) 4. STRATIFIED RATIO-TYPE IMPUTATION UNDER RANKED SET SAMPLING (RSS) 4. NUMERICAL STUDIES ACKNOWLEDGMENT REFERENCES Chapter 8: A Review of Bootstrap Methods in Ranked Set Sampling ABSTRACT INTRODUCTION RESAMPLING METHODS FOR RANKED SET SAMPLES CONSTRUCTION OF BOOTSTRAP CONFIDENCE INTERVALS SIMULATION STUDY DATA EXAMPLE DISCUSSION REFERENCES KEY TERMS AND DEFINITIONS Chapter 9: Fuzzy-Weighted Ranked Set Sampling Method ABSTRACT INTRODUCTION FUZZY-WEIGHTED RANKED SET SAMPLING SIMULATION STUDIES REAL DATA APPLICATIONS CONCLUSION AND FUTURE RESEARCH DIRECTIONS ACKNOWLEDGMENT REFERENCES ADDITIONAL READING Chapter 10: Stratified Ranked Set Sampling With Missing Observations for Estimating the Difference ABSTRACT 1. INTRODUCTION 2. THE STRATIFIED SIMPLE RANDOM SAMPLING (SSRS) ESTIMATOR OF D 3 STRATIFIED RSS (SRSS) ESTIMATION OF D 4. NUMERICAL COMPARISON OF SSRS AND SRSS MONTE CARLO EXPERIMENT 5. CONCLUSION ACKNOWLEDGMENT REFERENCES Compilation of References Related References About the Contributors
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