“There are many books covering various aspects of nonparametric inference, such as density estimation, nonparametric regression, bootstrapping, and wavelet methods. However, it can be challenging to find all these topics covered in a single resource. The goal of this text is to provide readers with a concise overview of modern nonparametric inference topics. The book targets master’s-level or Ph.D. students in statistics and computer science, as well as researchers in statistics, machine learning, and data mining who want to quickly grasp modern nonparametric methods.”

My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. 

“Of course, I had to select topics to include and omit, despite the title. For the most part, I decided to exclude topics that are too extensive to cover in a single chapter. For instance, I did not delve into classification or nonparametric Bayesian inference. The book evolved from my lecture notes for a half-semester (20 hours) course primarily attended by master’s-level students.” 

Table of Contents

1. Introduction
2. Estimating the CDF and Statistical Functionals
3. The Bootstrap and the Jackknife
4. Smoothing: General Concepts
5. Nonparametric Regression
6. Density Estimation
7. Normal Means and Minimax Theory
8. Nonparametric Inference Using Orthogonal Functions
9. Wavelets and Other Adaptive Methods
10. Other Topics

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 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)

About the Author

Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, 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 mathematiques 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.