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Total Size:
3.9 MB
Info Hash:
3610F124F4EEC7B626217726E8C8026D8C11315C
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Added:
Oct. 2, 2025, 11:02 a.m.
Stats:
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(Last updated: Oct. 2, 2025, 11:03 a.m.)
| File | Size |
|---|---|
| Kim J. Statistics in Survey Sampling 2025.pdf | 3.9 MB |
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42.5 MB
[27
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9]
2023-07-01
| Uploaded by indexFroggy | Size 42.5 MB | Health [ 27 /9 ] | Added 2023-07-01 |
NOTE
SOURCE: Kim J. Statistics in Survey Sampling 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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
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