Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field. Series: Springer Series in Statistics. Num Pages: 224 pages, biography. BIC Classification: KCH; PBT; UYA; UYQP; UYS. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 12. Weight in Grams: 355. . 2010. Softcover reprint of hardcover 1st ed. 2009. paperback. . . . . Books ship from the US and Ireland.

Bestandsnummer des Verk�ufers V9781441927095

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Titel: Introduction to Nonparametric Estimation
Verlag: Springer
Erscheinungsjahr: 2010
Sprache: Englisch
ISBN-10: 1441927093
ISBN-13: 9781441927095
Einband: Softcover
Zustand: New

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This book will be a valuable reference for researchers in the eare of nonparametrics.

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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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