Larry Wasserman

All of Nonparametric Statistics (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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. His research areas include nonparametric inference, asymptotic theory, multiple testing, 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 mathématiques 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. He is the author of All of Statistics: A Concise Course in Statistical Inference (Springer, 2003).

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 270
• Erscheinungstermin: 10. September 2006
• Englisch
• ISBN-13: 9780387306230
• Artikelnr.: 37288383

Autorenporträt

Aimed at Masters or PhD level students in statistics, computer science, and engineering, 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. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. With an exhaustive exploration of asymptotic nonparametric inferences, it also covers a huge range of other crucial topic areas 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.

Inhaltsangabe

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.

Rezensionen

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)