Nonparametric Functional Data Analysis

Theory and Practice de

,

Éditeur :

Springer


Collection :

Springer Series in Statistics

Paru le : 2006-11-22

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Description
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.
Pages
260 pages
Collection
Springer Series in Statistics
Parution
2006-11-22
Marque
Springer
EAN papier
9780387303697
EAN PDF
9780387366203

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
2427 Ko
Prix
126,59 €