Statistics in Survey Sampling
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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 intr...
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 samplingThorough exploration of model-assisted estimation techniques using auxiliary dataCoverage of modern topics including data integration, analytic inference, predictive inference, and voluntary sample analysisDetailed examples illustrate the methods throughout the bookFocused development within the frequentist framework, with limited emphasis on Bayesian or nonparametric methodsExercises in all chapters enable use as a course text or for self-studyIncludes 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.
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 samplingThorough exploration of model-assisted estimation techniques using auxiliary dataCoverage of modern topics including data integration, analytic inference, predictive inference, and voluntary sample analysisDetailed examples illustrate the methods throughout the bookFocused development within the frequentist framework, with limited emphasis on Bayesian or nonparametric methodsExercises in all chapters enable use as a course text or for self-studyIncludes 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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