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E-raamat: Statistics in Survey Sampling

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Offers a comprehensive and rigorous introduction to the principles and practices of survey sampling, bridging the gap between statistical theory and real-world data collection.



Statistics in Survey Sampling offers a comprehensive and rigorous introduction to the principles and practices of survey sampling. Bridging the gap between statistical theory and real-world data collection, this textbook presents both classical methods and modern developments, equipping readers with the tools to design effective surveys and make reliable inferences from sample data.

With a strong foundation in design-based inference and frequentist methodology, the book emphasizes representativeness, efficiency, and the integration of auxiliary information in estimation procedures. It also introduces emerging research topics that reflect the evolving landscape of data collection and analysis.

Key Features:

  • Rigorous treatment of statistical theory for design-based inference in probability sampling
  • Thorough exploration of model-assisted estimation techniques using auxiliary data
  • Coverage of modern topics including data integration, analytic inference, predictive inference, and voluntary sample analysis
  • Detailed examples illustrate the methods throughout the book
  • Focused development within the frequentist framework, with limited emphasis on Bayesian or nonparametric methods
  • Exercises in all chapters enable use as a course text or for self-study
  • Includes appendices on key background topics such as asymptotic theory and projection techniques

This textbook is ideal for graduate students in statistics with prior courses in statistical theory and linear models. It is also a valuable reference for researchers and practitioners engaged in survey design, public policy evaluation, official statistics, and data science applications involving sample-based inference.

1. Introduction.
2. Horvitz-Thompson Estimation.
3. Simple and
Systematic Sampling Designs.
4. Stratified Sampling.
5. Sampling with Unequal
Probabilities.
6. Cluster Sampling: Single-stage cluster sampling.
7. Cluster
Sampling: Two-stage cluster sampling.
8. Estimation: Part
1. 9. Estimation:
Part
2. 10. Variance Estimation.
11. Two-Phase Sampling.
12. Unit
Nonresponse.
13. Imputation.
14. Analytic Inference.
15. Predictive
Inference.
16. Analysis of Non-Probability Samples. A. Asymptotic Theory in
Survey Sampling. B. Projection Technique. Bibliography. Index.
Jae Kwang Kim is the LAS Deans Professor in the Department of Statistics at Iowa State University. He is a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He is the recipient of the 2015 Gertude M. Cox award, sponsored by the Washington Statistical Society and RTI international.