Based on the authors' lecture notes, the book is self-contained, which 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.
Features:
- Second edition has been updated with a new chapter on Nonparametric Estimation; a significant update to the chapter on Statistical Decision Theory; and other updates throughout
 - No requirement for heavy calculus, and 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
 - Self-contained, and can be used by the students to understand the theory
 - Chapters and sections marked by asterisks contain more advanced topics and may be omitted
 - Special chapters on linear models and nonparametric statistics show how the main theoretical concepts can be applied to well-known and frequently used statistical tools
 
The primary audience for the book is students who want to understand the theoretical basis of mathematical statistics-either advanced undergraduate or graduate students. It will also be an excellent reference for researchers from statistics and other quantitative disciplines.
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