Summary and Info
[Updated: Aug 17th. After this update, I would downgrade this book to 3 stars, which is still "good" under my rating system.]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 hugely 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, which is superior in my opinion.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.
More About the Author
Larry A. Wasserman is a Canadian statistician and a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University.
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All of Nonparametric Statistics (Springer Texts in Statistics) 0 out of 5 stars based on 0 ratings.