Advanced Sampling Methods - Latpate, Raosaheb

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Résumé :
This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.

Biographie:
RAOSAHEB LATPATE is Assistant Professor at the Department of Statistics and Center for Advanced Studies, Savitribai Phule Pune University, Pune, India. He completed his Ph.D. from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India. He has organized three workshops on statistical methods and applications and is a member of a number of professional societies and institutions, including the International Statistical Institute, International Indian Statistical Association, Society for Statistics and Computer Applications, and the Indian Society for Probability and Statistics. His research interests include genetic algorithm, fuzzy set theory, supply chain management, logistics and transportation problem, simulation and modelling, and sample survey. JAYANT KSHIRSAGAR is Principle at Ekta Shikshan Prasarak Mandal's Arts, Commerce and Science College, Ahmednagar, India. Earlier, he was Associate Professor of Statistics in New Arts, Commerce and Science College,Ahmednagar, India. He has been teaching statistics to undergraduate students for over 38 years and 8 years to postgraduate students. He has published over 15 research papers in reputed national and international journals and presented research papers in national and international conferences. His research interests include sampling designs, bio-statistics and mathematical statistics. VINOD KUMAR GUPTA is former ICAR National Professor of Indian Agricultural Statistics Research Institute, New Delhi, India. He received his Ph.D. in Agricultural Statistics from Indian Agricultural Research Institute (IARI), New Delhi, in 1983. His areas of research are design of experiments, theory of survey sampling and applied statistics. He has authored over 140 research papers, 4 books, 30 research project reports, several monographs, teaching manuals, electronic manuals, and popular articles, and more than 10 special volumes of journals as guest editor. Under his supervision, 11 students have completed their Ph.D. degrees. He is associated with the developments of two very important and popular web resources, namely, Design Resources Server and Sample Surveys Resources Server. He is President of the Society of Statistics, Computer and Applications, New Delhi, India, and President of the Governing Body of Institute of Applied Statistics and Development Studies, Lucknow, India. A Fellow of the National Academy of Agricultural Sciences and that of the Indian Society of Agricultural Statistics, India, Dr. Gupta is the elected member of the International Statistical Institute, the Netherlands. He has been awarded the Sankhyiki Bhushan Award from the Institute of Applied Statistics and Development Studies, India...

Sommaire:
-1. Introduction.- 2. Simple Random Sampling.- 3. Stratied Random Sampling.- 4. Cluster Sampling.- 5. Double Sampling.- 6. Probability Proportional to Size Sampling.- 7. Systematic Sampling.- 8. Resampling Techniques.- 9. Adaptive Cluster Sampling.- 10. Two-Stage Adaptive Cluster Sampling.- 11. Adaptive Cluster Double Sampling.- 12. Inverse Adaptive Cluster Sampling.- 13. Two Stage Inverse Adaptive Cluster Sampling.- 14. Stratified Inverse Adaptive Cluster Sampling.- 15. Negative Adaptive Cluster Sampling.- 16. Negative Adaptive Cluster Double Sampling.- 17. Two- Stage Negative Adaptive Cluster Sampling.- 18. Balanced and Unbalanced Ranked Set Sampling.- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference.- 20. Important Versions of Ranked Set Sampling.- 21. Sampling Errors.