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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.
Meaning and Purpose of Survey Sampling
Inference in Survey Sampling
Sampling with Varying Probabilities
Fixing the Size of an Equal Probability Sample
Adjusting Unit-Nonresponse by Weighting and Tackling Item-Nonresponse by Imputation
Randomized Response and Indirect Survey Techniques
Super-Population Modeling. Model-Assisted Approach. Asymptotics
Prediction Approach: Robustness, Bayesian Methods, Empirical Bayes
Small Area Estimation and Developing Small Domain Statistics
Estimation of Non-linear Parametric Functions
Permanent Random Numbers, Poisson Sampling and Collocated Sampling
Network and Adaptive Sampling
Fixing Size of a Sample in Complex Strategies
Inadequate and Multiple Frame Data and Conditional Inference
Study of Analytical Surveys