Introduction to Modern Sampling Theory. Designs, estimators and algorithms

      Аннотация: 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.

Ключевые слова: Statistical inference, Probability, Statistical inference, Statistics, Probability, Survey Sampling, random sampling, sampling algorithm, balanced sampling