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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.
- Sales Rank: #198079 in Books
- Published on: 2007-05-22
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .69" w x 6.14" l, 1.16 pounds
- Binding: Hardcover
- 270 pages
Review
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, 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, 49:1, February 2007)
"The intention of this book is to give a single source with brief accounts of modern topics in nonparametric inference. … The text is a mixture of theory and applications, and there are lots of examples … . The text is also illustrated with many informative figures. … this book covers many topics of modern nonparametric methods, with focus on estimation and on the construction of confidence sets. It should be a useful reference for anyone interested in the theories and methods of this area." (Andreas Karlsson, Statistical Papers, 48, 2006)
"...ANPS provides an excellent complement or a complete course textbook with a mixture of theoretical and computational exercises. ...For a book in�a rapidly evolving field, the content and references are quit eup to date. ...As advertised, it offers a well-written, albeit brief account of numerous topics in modern nonparametric inference." (Greg Ridgeway, Journal of the American Statistical Association, Vol. 102, No. 477, 2007)
"This is a nicely written textbook oriented mainly to master level statistics and computer science students. The author provides wide a coverage of modern nonparametric methods … . the key ideas and basic proofs are carefully explained. Bibliographic remarks point the reader to references that contain further details. Each chapter is finished with useful exercises … . The book is also suitable for researchers in statistics, machine learning, and data mining." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1099 (1), 2007)
From the Back Cover
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 math�matiques 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).
Most helpful customer reviews
42 of 43 people found the following review helpful.
I don't think it is quite all of nonparametrics but good topics well presented in brief
By Michael R. Chernick
This is a very nice and concisely written account of the field of nonparametrics. It is very bold for an author to use "all" in the title and I am sure that Wasserman does this with tongue in cheek. He has been even bolder with his other book called "All of Statistics." However, I think that it can safely be said that the essentials are covered along with many interesting topic that are not covered in a traditional nonparametrics text such as Conover's book.
The author's intentions are best expressed in this excerpt from the publisher's description. "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."
So we see that by "all" the author means a brief description of most topics in nonparametrics. Nonparametric density estimation, bootstrap, nonparametric regression, and wavelets are not covered in traditional nonparametric books and the topics are so involved that each has been treated in books solely dedicated to that topic. I and at least five other authors have written books dedicated to the bootstrap and other resampling methods. Hardle and others have written books on nonparametic regression and Silverman and others have published books on univariate nonparametric density estimation. Scott has done one on multivariate nonparametric density estimation. The topics are impressive and the coverage is good but obviously not thorough. To thoroughly treat all of these topics would take several thousands of pages!
9 of 11 people found the following review helpful.
Covers modern material but without code support
By David
I've read about 75% of this book, quitting near the end where the material was of less interest to me. I found this book to be largely focused the theory of nonparametrics with examples. The most important aspect of the book relative to others is that it teaches where each of the methods comes from in a rigorous fashion, i.e. it provides a wonderful base in theory of modern nonparametric methods.
On the other hand, those who want to actually apply the methods will be at a bit of a loss unless they are comfortable with scavenging CRAN for the proper functions/packages for the methods (and are successful at picking good packages). Or if you want to reinvent the wheel, that also works. I was disappointed in the practical aspect of the book in applying the methods. In the book preface, it says "data sets and code may be found at [web address]"... there is data but no code there (as of 8/17/09). At the very least, suggest package/functions in R (or some software)! Just the package/function names would be enough to get running on applying the methods and is certainly not too much to ask.
One other complaint I have with this book is that there is too much time spent on basic concepts while too little on the more complex concepts (as is often the case). The complex material is within reach if I reread it many times, however, more pages spent on these materials with corresponding less on the basics would have been preferable and resulted in a superior text.
For practitioners, I don't think this book will meet expectations. Practitioners open to other methods might instead check out The Elements of Statistical Learning.
That said, I am pleased with this book for its theory (it made me greatly appreciate jackknifing), and I would recommend this book to anyone interested in developing nonparametrics or who wants to understand some intricate nonparametric methods. It is well-written in what I anticipate was its aim: provide a strong theory base for modern nonparametric methods. For those with a milder interest, this book probably won't meet expectations.
1 of 1 people found the following review helpful.
Five Stars
By Steven M. Anderson
Wonderful overview of nonparametric statistics. Great resource for looking things up when you need them.
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