Statistical theory : a concise introduction / Felix Abramovich, Ya'acov Ritov.

Includes bibliographical references and index.

Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs. Based on the authors' lecture notes, this student-oriented, self-contained book maintains a proper balance between the clarity and rigor of exposition. In a few cases, the authors present a'sketched' version of a proof, explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments. Chapters and sections marked by asterisks contain more advanced topics and may be omitted. A special chapter on linear models shows how the main theoretical concepts can be applied to the well-known and frequently used statistical tool of linear regression. Requiring no heavy calculus, simple questions throughout the text help students check their understanding of the material. Each chapter also includes a set of exercises that range in level of difficulty. Features: Delineates the fundamental concepts of statistical theory, Balances exposition and mathematical formality, Introduces topics with numerous examples, avoiding a dry approach to the subject, Assumes an intermediate-level background in calculus and probability, Reviews the necessary probabilistic material in an appendix, Includes a series of exercises of varying levels of difficulty in each chapter, with selected solutions in an appendix Book jacket.