Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. This book starts from theoretical foundations including functional nonparametric modeling, description of the mathematical framework, construction of the statistical methods, and statements of their asymptotic behaviors. It proceeds to computational issues including R and S-PLUS routines. Several functional datasets in chemometrics, econometrics, and pattern recognition are used to emphasize the wide scope of nonparametric functional data analysis in applied sciences. The companion Web site includes R and S-PLUS routines, command lines for reproducing examples presented in the book, and the functional datasets.
Rather than set application against theory, this book is really an interface of these two features of statistics. A special effort has been made in writing this book to accommodate several levels of reading. The computational aspects are oriented toward practitioners whereas open problems emerging from this new field of statistics will attract Ph.D. students and academic researchers. Finally, this book is also accessible to graduate students starting in the area of functional statistics.
| ISBN: | 9781441921413 |
| Publication date: | 24th November 2010 |
| Author: | Frédéric Ferraty, Philippe Vieu |
| Publisher: | Springer an imprint of Springer New York |
| Format: | Paperback |
| Pagination: | 268 pages |
| Series: | Springer Series in Statistics |
| Genres: |
Probability and statistics Stochastics Maths for computer scientists Econometrics and economic statistics Applied mathematics Earth sciences The environment |
Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. This book starts from theoretical foundations including functional nonparametric modeling, description of the mathematical framework, construction of the statistical methods, and statements of their asymptotic behaviors. It proceeds to computational issues including R and S-PLUS routines. Several functional datasets in chemometrics, econometrics, and pattern recognition are used to emphasize the wide scope of nonparametric functional data analysis in applied sciences. The companion Web site includes R and S-PLUS routines, command lines for reproducing examples presented in the book, and the functional datasets.
Rather than set application against theory, this book is really an interface of these two features of statistics. A special effort has been made in writing this book to accommodate several levels of reading. The computational aspects are oriented toward practitioners whereas open problems emerging from this new field of statistics will attract Ph.D. students and academic researchers. Finally, this book is also accessible to graduate students starting in the area of functional statistics.
Nonparametric Functional Data Analysis features in the following genres: Probability and statistics, Stochastics, Maths for computer scientists, Econometrics and economic statistics, Applied mathematics, Earth sciences, The environment
Nonparametric Functional Data Analysis is available in Paperback, Hardback
Nonparametric Functional Data Analysis was written by Frédéric Ferraty, Philippe Vieu and published by Springer an imprint of Springer New York
Nonparametric Functional Data Analysis has 268 pages
Yes it is part of Springer Series in Statistics series