A state-of-the-art edited survey covering all aspects of sampling theory. Theory, methods and applications are discussed in authoritative expositions ranging from multi-dimensional signal analysis to wavelet transforms. The book is an essential up-to-date resource.

This book provides an introduction to modern sampling survey theory. The author proposed an original approach based on an index free formalism. This formulation turns out to be ideal for taking advantage of many modern statistical software (such as Matlab or R), which allow managing vectors and data matrix as single algebraic objects. The book represents an introduction covering all the topics usually treated in standard sample survey books, such as: random samples and sampling designs, estimators and their properties, non-response. Moreover, even though sampling algorithm theory is not presented systematically, the author provides a large set of codes and examples allowing the reader to implement almost every sampling scheme discussed within the book. Only basic knowledge of algebra, calculus, probability and programming, that are within the academic curriculum of any scientific graduated student, is required. The aim of the book is to provide all the basic scientific knowledge and technical tools needed to start projecting and developing a modern sample survey.

The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sampling. Basic knowledge of probability theory and statistical inference is assumed, but no prior exposure to survey sampling is required. The first part focuses on the design-based approach to finite population sampling. It contains a rigorous coverage of basic sampling designs, related estimation theory, model-based prediction approach, and model-assisted estimation methods. The second part stems from original research conducted by the authors as well as important methodological advances in the field during the past three decades. Topics include calibration weighting methods, regression analysis and survey weighted estimating equation (EE) theory, longitudinal surveys and generalized estimating equations (GEE) analysis, variance estimation and resampling techniques, empirical likelihood methods for complex surveys, handling missing data and non-response, and Bayesian inference for survey data. The third part provides guidance and tools on practical aspects of large-scale surveys, such as training and quality control, frame construction, choices of survey designs, strategies for reducing non-response, and weight calculation. These procedures are illustrated through real-world surveys. Several specialized topics are also discussed in detail, including household surveys, telephone and web surveys, natural resource inventory surveys, adaptive and network surveys, dual-frame and multiple frame surveys, and analysis of non-probability survey samples. This book is a self-contained introduction to survey sampling that provides a strong theoretical base with coverage of current research trends and pragmatic guidance and tools for conducting surveys.