Cover of: Introduction to nonparametric estimation | Alexandre B. Tsybakov

Introduction to nonparametric estimation

  • 214 Pages
  • 1.41 MB
  • 9350 Downloads
  • English
by
Springer , New York, London
Nonparametric statistics, Estimation t
StatementAlexandre B. Tsybakov.
SeriesSpringer series in statistics
Classifications
LC ClassificationsQA278.8 .T79 2009
The Physical Object
Paginationxii, 214 p. ;
ID Numbers
Open LibraryOL23696782M
ISBN 100387790519, 0387790527
ISBN 139780387790510, 9780387790527
LC Control Number2008939894

This item: Introduction to Nonparametric Estimation (Springer Series in Statistics) by Alexandre B. Tsybakov Hardcover $ All of Nonparametric Statistics (Springer Texts in Statistics) by Larry Wasserman Hardcover $ Statistical Inference by George Casella Hardcover $ Customers who viewed this item also viewedCited by: 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. Introduction 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 Introduction to nonparametric estimation book. Introduction to Nonparametric Estimation book.

Read reviews from world’s largest community for readers. This is a revised and extended version of the Fre /5(7). 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.".

Appropriate for a one-semester course, this self-contained book is an introduction to nonparametric curve estimation theory. Introduction to nonparametric estimation book book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice ().

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 book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, one-sample test to k-sample tests, etc.

The two methods of statistics are presented simultaneously, with indication of their use in data analysis. The first nonparametric regression estimate of local averaging type was proposed by J.

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Tukey in The partitioning regression es- timate he introduced, by analogy to the classical partitioning (histogram) density estimate, can be regarded as a special least squares estimate. Notes for Nonparametric Statistics Chapter 1 Introduction We begin by reviewing some elementary results that will be employed during the course, which will also serve to introduce notation.

There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods.

But it is hard to find all these topics covered in one place. The goal of this text is to provide readers with a single book where they can find a. for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric.

This book provides a complete analysis of educational production and costs using the nonparametric technique known as Data Envelopment Analysis (DEA).

The book focuses on estimation of technical, allocative and scale efficiency in the public sector characterized by the influence of exogenous socio-economic variables. Recent Advances and Trends in Nonparametric Statistics. Book • Select An Introduction to Support Vector Machines.

Book chapter Full text access. particularly in the areas of nonparametric estimation of diffusion processes, nonparametric testing of parametric diffusion models, and nonparametric pricing of derivatives. Buy Introduction to Nonparametric Estimation (Springer Series in Statistics) 1st Edition. 2nd Printing.

by Tsybakov, Alexandre B. (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on eligible s: 2. This book concentrates on the statistical aspects of nonparametric regression smoothing from an applied point of view. The methods covered in this text can be used in biome.

Abstract A statistical method is called non-parametric if it makes no assumption on the population distribution or sample size. This is in contrast with most parametric methods in elementary. Nonparametric estimation of the determinants of inefficiency.

Details Introduction to nonparametric estimation EPUB

Journal of Productivity Analysis, Vol. 47, Issue. 3, p. 'This well-written textbook represents a rigorous yet accessible introduction to nonparametric methods, one that makes clear the importance of these techniques for empirical research.

'The aim of this book is to. Buy Introduction to Nonparametric Estimation (Springer Series in Statistics) Softcover reprint of hardcover 1st ed. by Tsybakov, Alexandre B. (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on eligible s: 2. Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications.

The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics. This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis.

The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is. Introduction to Nonparametric Estimation (Springer Series in Statistics) by [Tsybakov, Alexandre B.

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.

Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level.

Introduction to Nonparametric Estimation: Tsybakov, Alexandre B.: Books - Get this from a library. Introduction to nonparametric estimation.

[A B Tsybakov] -- Presents basic nonparametric regression and density estimators and analyzes their properties. This book covers minimax lower bounds, and develops advanced topics such as: Pinsker's theorem, oracle. A statistical method is called non-parametric if it makes no assumption on the population distribution or sample size.

This is in contrast with most parametric methods in elementary statistics that assume that the data set used is quantitative, the. Buy a cheap copy of Introduction to Modern Nonparametric book by James J.

Higgins. Guided by problems that frequently arise in actual practice, James Higgins book presents a wide array of nonparametric methods of data analysis that researchers Free shipping over $ A catalog record for this book is available from the Library of Congress.

Description Introduction to nonparametric estimation FB2

ISBN: This book is printed on acid-free paper. Headquarters Marcel Dekker, Inc. Madison Avenue, New York, NY tel: ; fax: Eastern Hemisphere Distribution Marcel Dekker AG Hutgasse 4, PostfachCH Basel, Switzerland.

Closing an important gap in the literature, it contains not only classical material on nonparametric estimation of monotone functions in a series of application fields but also an introduction to advanced themes that are the topic of active ongoing research - in particular, estimation of convex functions, interval censoring, higher dimensional.

Welcome. This course is intended to provide an introduction to nonparametric estimation of the density and regression functions from, mostly, the perspective of kernel smoothing.

The emphasis is placed in building intuition behind the methods, gaining insights into their asymptotic properties, and showing their application through the use of statistical software.An introduction to nonparametric adaptive estimation Gaëlle Chagny Abstract Statistical estimation aims at building procedures to recover unknown parameters by analysing some measured data sam-pled from a large population.

This note deals with the case of in nite dimensional parameters, ypicallyt functions, through the example of probability."The aim of this book is to teach nonparametric methods to applied economists. The book does an excellent job of achieving this objective.

The mix of rigor and intuition is perfect, and the availability of software to go with the book makes it easy to implement the techniques being taught." Peter Schmidt, Michigan State University. From the Price: $