Introduction to nonparametric estimation
QA278.8 .T7913 2009
- Unknown QA278.8 .T7913 2009
- Zai͡at͡s, Vladimir.
- Includes bibliographical references (p. -209) and index.
- "Methods of nonparametric estimation are located at the core of modern statistical science. The aim of this book is to give a short but mathematically self-contained introduction to the theory of nonparametric estimation. The emphasis is on the construction of optimal estimators; therefore the concepts of minimax optimality and adaptivity, as well as the oracle approach, occupy the central place in the book." "This is a concise text developed from lecture notes and ready to be used for a course on the graduate level. The main idea is to introduce the fundamental concepts of the theory while maintaining the exposition suitable for a first approach in the field. Therefore, the results are not always given in the most general form but rather under assumptions that lead to shorter or more elegant proofs." "The book has three chapters. Chapter 1 presents basic nonparametric regression and density estimators and analyzes their properties. Chapter 2 is devoted to a detailed treatment of minimax lower bounds. Chapter 3 develops more advanced topics: Pinsker's theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity."--BOOK JACKET.
- Publication date
- Alexandre B. Tsybakov.
- Springer series in statistics
- "The French edition of this work that is the basis of this work that is the basis of this expanded edition was translated by Vladimir Zaiats."--P. [i].
- Publisher Number