## Statistical Theory: A Concise IntroductionAbramovich, F. and Ritov, Y. (2013).Chapman & Hall/CRC Summary | Errata | Review | Link to publisher |

Summary: 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. |

- statistical learning
- high-dimensional inference, sparsity, model selection procedures
- nonparametric curve estimation and related problems
- wavelets in statistics

- Associate Editor,
*Electronic Journal of Statistics*, 2007-2021 - Associate Editor,
*Annals of Statistics*, 2007-2009

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