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.
Review
.,."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)
Summary
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 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, 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)
Table of Contents
Introduction
Estimating the CDF and Statistical Functions
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 Means.
