All of Nonparametric Statistics

This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD 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. It 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's dual approach includes a mixture of methodology 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, 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 contain further 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)

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 contain further 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)

This book covers many areas of nonparametric statistics.

There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master's-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric 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 have had to choose topics to include andto omit, the title notwithstanding. For the mostpart, I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master's-level students. For Ph. D.

This comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. The book's dual approach includes a mixture of methodology and theory.

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.