About this ebook
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.
About the author
Jae Kwang Kim is the LAS Dean’s 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.
