As a comprehensive textbook in survey sampling, this book discusses the inadequacies of classic, designed-based inferential procedures and provides alternative approaches in the form of model formulations, model-design-based procedures of analysis, inference and interpretation. The book focuses on a wide range of topics which included Bayesian and Empirical Bayesian approaches, complex procedures of stratification, clustering, sampling in multi stages and phases, linear and non-linear estimation of parameters, small area estimation by spatial and chronological modelling, network and adaptive sampling methods and more. The book includes detailed case studies and exercises, making it valuable for students of statistics, specifically survey sampling.

Autorentext

ARIJIT CHAUDHURI is an honorary visiting professor at the Applied Statistics Unit at the Indian Statistical Institute (ISI), Kolkata, India, since 1st September 2005. Professor Chaudhuri holds a Ph.D. in Statistics in the area of sample surveys from the University of Calcutta, Kolkata, India, from where he also has graduated. He worked as a postdoctoral researcher for two years at the University of Sydney (1973-1975), Australia. He retired as a professor from the ISI, Kolkata, India, on 31st August 2002, where he then continued to work as a CSIR emeritus scientist for three years up to 31st August 2005. His areas of research include mean square error estimation in multi-stage sampling, analytical study of complex surveys, randomized response surveys, and small area estimation. In the year 2000, he was elected as the President of the Section of Statistics for the Indian Science Congress and worked for the Government of West Bengal for 12 years as the Committee Chairman for the improvement of crop statistics. He has also worked with the Government of India to apply sophisticated methods of small area estimation in improving state and union territory level estimates of various parameters of national importance. He has worked on various global assignments upon invitation, including visiting professorships at several universities and statistical offices in the USA, Canada, England, Germany, the Netherlands, Sweden, Israel, Cyprus, Turkey, Cuba, and South Africa, from 1979 to 2009. He has successfully mentored 10 Ph.D. students and published more than 150 research papers in peer-reviewed journals, a few of them jointly with his students and colleagues. He is the author of 11 books on survey sampling. SANGHAMITRA PAL is an assistant professor at the Department of Statistics, West Bengal State University (WBSU), India, since 2009. She completed her Ph.D. in Statistics from the Indian Statistical institute (ISI), Kolkata, in 2004. Earlier, she served as a research scientist from 2001 to 2009 at River Research Institute (RRI), the Government of West Bengal, India. She also worked as a research associate and a visiting scientist at the Applied Statistics Unit, ISI, Kolkata, during January 2005 to July 2006. She is guiding two Ph.D. students in the area of sample surveys at WBSU and has published research articles in several reputed journals. She organized ab invited session on the adaptive cluster sampling at the International Statistical Institute Conference in Durban, South Africa (2009); a sample survey session at the Indian Science Congress (2012); and presented papers at the international conferences in South Africa, New Zealand, Germany, France, Thailand, Singapore, as well as India. She was involved in the professional attachment training (PAT) program of sample survey with ICAR-NAARM, Hyderabad, India. Besides teaching and research, Dr. Pal is working as a nodal officer for the All India Survey of Higher Education cell of WBSU.

Inhalt

1. Meaning and purpose of Survey Sampling.- 2. Inference in Survey Sampling.- 3. Sampling with Varying Probabilities.- 4. Fixing the size of an Equal Probability Sample.- 5. Adjusting Unit-Nonresponse by weighting & Tackling Item-Nonresponse by Imputation.- 6. Randomized Response & Indirect Survey Techniques.- 7. Super-Population Modeling. Model-Assisted Approach.Asymptotics.- 8. Prediction Approach: Robustness, Bayesian Methods, Empirical Bayes.- 9. Small Area Estimation & Developing Small Domain Statistics.- 10. Estimation of non-linear Parametric functions.- 11. Permanent random numbers, Poisson Sampling and Collocated Sampling.- 12. Network and Adaptive Sampling.- 13. Fixing size of a sample in Complex Strategies.- 14. Inadequate and Multiple Frame Data and Conditional Inference.- 15. Study of Analytical Surveys.- An Epilogue.- References.