All of Nonparametric Statistics (Springer Texts in Statistics) (Hardcover)

by Larry Wasserman (Author)
Key Phrases: pointwise asymptotic confidence interval, using local linear regression, hard threshold estimator, Bibliographic Remarks, General Concepts, Other Topics (more...)

Editorial Reviews

Review
From the reviews:

.,."The book is excellent." (Short Book Reviews of the ISI, June 2006)

"Now we have All of Nonparametric Statistics a ] the writing is excellent and the author is to be congratulated on the clarity achieved. a ] 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. a ] The text is a mixture of theory and applications, and there are lots of examples a ] . The text is also illustrated with many informative figures. a ] 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 masterlevel statistics and computer science students. The author provides wide a coverage of modern nonparametric methods a ] . 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 a ] . The book is also suitable for researchers in statistics, machine learning, and data mining." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1099 (1), 2007)

Product 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. 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)

Product Details

• Hardcover: 268 pages
• Publisher: Springer (May 22, 2007)
• Language: English
• ISBN-10: 0387251456
• ISBN-13: 978-0387251455
• Product Dimensions: 9.5 x 6.4 x 0.8 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #259,213 in Books (See Bestsellers in Books)

  Popular in this category: (What's this?)

  #98 in

  Books > Computers & Internet > Computer Science > Artificial Intelligence > Computer Mathematics

Customer Reviews

Most Helpful Customer Reviews

33 of 33 people found the following review helpful:

5.0 out of 5 stars I don't think it is quite all of nonparametrics but good topics well presented in brief, March 27, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

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!

1 of 1 people found the following review helpful:

4.0 out of 5 stars Great coverage of modern material, June 26, 2009

By	David Diez (Los Angeles, CA) - See all my reviews (TOP 1000 REVIEWER)

I've been reading (and rereading) this book on-and-off for a couple of months. Overall, I've found it to be written clearly and very concisely, and each reread offers me additional insights. While I was overwhelmed with some of the theoretical material at first (which I'm finally starting to wrap my head around), I found that this wasn't necessarily a barrier to understanding many of the later sections of the book. While this book won't really offer the depth that might be desired for some readers, it does suggest additional sources for further reading in areas of particular interest.

My one complaint about the book is the lack of software discussion. Statistics has really developed into a computationally heavy field, especially in nonparametric methods. To not discuss formal implementation of the methods (presumably in R) leaves an incomplete feeling after reading about the methods and examples. I could always write my own code but I don't want to reinvent the wheel since most if not all of these methods are available in R packages for free (just point us to them!).

If you would like a rigorous introduction to the theory of many nonparametric methods, this would be a good book for you. If your interests are in understanding basics about methods and implementing them quickly, this text is probably not for you.

15 of 22 people found the following review helpful:

4.0 out of 5 stars 10-to-1 Equation to Text Ratio, August 19, 2007

By	Richard Deveno (Alameda, CA USA) - See all my reviews (REAL NAME)

My first thought upon receiving this slim volume went something along the lines of "Oh my, all of non-parametric statistics is much, much smaller than I expected". My other impressions are somewhat vague since (as the title of this review implies), this is not exactly a flip-through book for light reading. The content is dense ... like 100-year old ebony wood.

And after carefully examining the table of contents, I also noticed that the text has precious little to say about methods based on rank. So this is more or less a book on "modern methods". It's a very interesting selection of material - although I will probably never be able to read it.