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All of Nonparametric Statistics

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All of Nonparametric Statistics Cover

 

Synopses & Reviews

Publisher Comments:

The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory. From the reviews: "...The book is excellent." (Short Book Reviews of the ISI, June 2006) "Now we have All of Nonparametric Statistics ... . the writing is excellent and the author is to be congratulated on the clarity achieved. ... the book is excellent." (N.R. Draper, Short Book Reviews, Vol. 26 (1), 2006) "Overall, I enjoyed reading this book very much. I like Wasserman's intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences." (Stergios B. Fotopoulos for Technometrics, Vol. 49, No. 1., February 2007)

Synopsis:

This comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. The book's dual approach includes a mixture of methodology and theory.

Synopsis:

This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book's dual approach includes a mixture of methodology and theory.

Synopsis:

This book covers many areas of nonparametric statistics.

Table of Contents

Introduction.- Estimating the CDF and Statistical Functions.- The Bootstrap and the Jackknife.- Smoothing: General Concepts.- Nonparametric Regression.- Density Estimation.- Normal Means and Minimax Theory.- Nonparametric Inference Using Orthogonal Functions.- Wavelets and Other Adaptive Means.

Product Details

ISBN:
9780387251455
Author:
Wasserman, Larry
Publisher:
Springer
Author:
Cram101 Textbook Reviews
Location:
New York, NY
Subject:
Statistics
Subject:
Nonparametric statistics
Subject:
Probability & Statistics - General
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Statistical Theory and Methods
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences <P>Aimed at Masters or PhD level students in statistics, computer science, and engineering, this comprehensive text provides the reader with a single book where they can f
Subject:
Mathematics | Probability and Statistics
Subject:
Education-General
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Language, literature and biography
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Subject:
Artificial Intelligence
Copyright:
Edition Number:
1
Edition Description:
1st ed. 2005. Corr. 3rd. printing
Series:
Springer Texts in Statistics
Publication Date:
October 2005
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
280
Dimensions:
235 x 155 mm 1270 gr

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All of Nonparametric Statistics New Hardcover
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$180.25 In Stock
Product details 280 pages Springer - English 9780387251455 Reviews:
"Synopsis" by , This comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. The book's dual approach includes a mixture of methodology and theory.
"Synopsis" by , This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book's dual approach includes a mixture of methodology and theory.
"Synopsis" by , This book covers many areas of nonparametric statistics.
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