All of Nonparametric Statistics

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 containfurther 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)

Estimating the CDF and Statistical Functionals.- 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 Methods.- Other Topics.