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All of Nonparametric Statistics (Springer Texts in Statistics)

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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. Larry Wasserman is Professor of Statistics at Carnegie Mellon University and a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, multiple testing, and applications to astrophysics, bioinformatics and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathématiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He is the author of All of Statistics: A Concise Course in Statistical Inference (Springer, 2003).

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.

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:
9781441920447
Author:
Wasserman, Larry
Publisher:
Springer
Location:
New York, NY
Subject:
Statistics
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:
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 Description:
Softcover reprint of hardcover 1st ed. 2006
Series:
Springer Texts in Statistics
Publication Date:
20101119
Binding:
TRADE PAPER
Language:
English
Pages:
280
Dimensions:
235 x 155 mm 428 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

All of Nonparametric Statistics (Springer Texts in Statistics) New Trade Paper
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Product details 280 pages Not Avail - English 9781441920447 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.
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