Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling.
The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students.
Prof. Raghunath Arnab is a Professor of Statistics, University of Botswana, Botswana and Honorary Professor of Statistics, University of KwaZulu-Natal, South Africa. Prof. Arnab received his Ph.D. degree in 1981 from the Indian Statistical Institute, Kolkata. He is a co-author of the book A new concept for tuning design weights in survey sampling (jointly with Prof. S. Singh, Prof. A. Sedory, Prof. M. der Mar Rueda, Prof. A. Arcos) and an author of numerous research articles, Associate editor of the Journal of Statistical Theory and Practice, Model Assisted statistics and its Applications, Journal of the Indian Society of Agricultural Statistics and Advances and Applications in Statistics. Prof. Arnab was an elected member of the International Statistical Institute, Life member of the International Statistical Institute and a member of the Biometric Society.
1. Preliminaries and Basics of Probability Sampling 2. Unified Sampling Theory: Design-Based Inference 3. Simple Random Sampling 4. Systematic Sampling 5. Unequal Probability Sampling 6. Inference Under Superpopulation Model 7. Stratified Sampling 8. Ratio Method of Estimation 9. Regression, Product, and Calibrated Methods of Estimation 10. Two-Phase Sampling 11. Repetitive Sampling 12. Cluster Sampling 13. Multistage Sampling 14. Variance/Mean Square Estimation 15. Nonsampling Errors 16. Randomized Response Techniques 17. Domain and Small Area Estimation 18. Variance Estimation: Complex Survey Designs 19. Complex Surveys: Categorical Data Analysis 20. Complex Survey Design: Regression Analysis 21. Ranked Set Sampling 22. Estimating Functions 23. Estimation of Distribution Functions and Quantiles 24. Controlled Sampling 25. Empirical Likelihood Method in Survey Sampling 26. Sampling Rare and Mobile Populations