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.
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.

Introduction to Nonparametric Estimation
214
Introduction to Nonparametric Estimation
214Paperback(Softcover reprint of hardcover 1st ed. 2009)
Product Details
ISBN-13: | 9781441927095 |
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Publisher: | Springer New York |
Publication date: | 11/29/2010 |
Series: | Springer Series in Statistics , #161 |
Edition description: | Softcover reprint of hardcover 1st ed. 2009 |
Pages: | 214 |
Product dimensions: | 6.10(w) x 9.10(h) x 0.50(d) |