Introduction to nonparametric estimation

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 concep...

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Autor principal: Tsybakov, Alexandre B., statisticien (Autor d'introducció, etc.)
Altres autors: Zaiats, Vladimir (Traductor)
Format: Livre numérique
Idioma:Anglais
Publicat: New York, NY : Springer New York [20..].
Cham : Springer Nature
Edició:1st ed. 2009.
Col·lecció:Springer Series in Statistics
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Edition sous un autre format:• Introduction to nonparametric estimation, Alexandre B. Tsybakov, 2009, New York, Springer, 1 vol. (XII-214 p.), Springer series in statistics, 978-0-387-79051-0
Descripció
Sumari: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.
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Bibliografia:Bibliogr.p. 201-207. Index
ISBN:9780387790527 (PDF)
ISSN:2197-568X
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