Introduction to Nonparametric Estimation - Springer Series in Statistics (Hardback)
  • Introduction to Nonparametric Estimation - Springer Series in Statistics (Hardback)

Introduction to Nonparametric Estimation - Springer Series in Statistics (Hardback)

Hardback 214 Pages / Published: 26/11/2008
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This is a revised and extended version of the French book. The main changes are in Chapter 1 where the former Section 1. 3 is removed and the rest of the material is substantially revised. Sections 1. 2. 4, 1. 3, 1. 9, and 2. 7. 3 are new. Each chapter now has the bibliographic notes and contains the exercises section. I would like to thank Cristina Butucea, Alexander Goldenshluger, Stephan Huckenmann, Yuri Ingster, Iain Johnstone, Vladimir Koltchinskii, Alexander Korostelev, Oleg Lepski, Karim Lounici, Axel Munk, Boaz Nadler, AlexanderNazin,PhilippeRigollet,AngelikaRohde,andJonWellnerfortheir valuable remarks that helped to improve the text. I am grateful to Centre de Recherche en Economie et Statistique (CREST) and to Isaac Newton Ins- tute for Mathematical Sciences which provided an excellent environment for ?nishing the work on the book. My thanks also go to Vladimir Zaiats for his highly competent translation of the French original into English and to John Kimmel for being a very supportive and patient editor. Alexandre Tsybakov Paris, June 2008 Preface to the French Edition The tradition of considering the problem of statistical estimation as that of estimation of a ?nite number of parameters goes back to Fisher. However, parametric models provide only an approximation, often imprecise, of the - derlying statistical structure. Statistical models that explain the data in a more consistent way are often more complex: Unknown elements in these models are, in general, some functions having certain properties of smoo- ness.

Publisher: Springer-Verlag New York Inc.
ISBN: 9780387790510
Number of pages: 214
Weight: 508 g
Dimensions: 235 x 155 x 14 mm
Edition: 1st Edition. 2nd Printing. 2008


From the reviews:

"The book is meant to be an introduction to the rich theory of nonparametric estimation through some simple models and examples. The detailed proofs given in the book will help the interested reader to understand the subject better. This well written book will be welcomed by all those interested in learning the presented concepts. The author should be complimented for a good treatise with detailed proofs of several important results in nonparametric estimation theory." (Ravi Sreenivasan, Zentralblatt MATH, Vol. 1176, 2010)

"...A short and rigorous introduction to minimax results for estimators of densities and regression functions from independent observations. Each of the three chapters ends with a section containing detailed biographical notes and a section with exercises complementing and illustrating the main results. This book is an excellent introduction to the results and techniques of minimax estimation." (Journal of the American Statistical Association, Vol. 105, No. 489)

"The potential reader of this book should be conversant with functional analysis and topology ... . for a broad spectrum of mathematical statisticians, especially in the Continental Europe, this will be welcome as a good reading material. ... attempts to formulate the basic theory of nonparametric functional estimation, including (i) construction of such estimators, (ii) their (asymptotic) statistical properties, (iii) optimality, in some sense, and (iv) adaptive estimation. The author contends to present the material ... in a broad sense more acceptable to statisticians." (Pranab K. Sen, International Statistical Review, Vol. 79 (2), 2011)

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