Functional Data Analysis

James Ramsay, B. W. Silverman

ISBN 10: 038740080X / ISBN 13: 9780387400808

Edité par Springer-Verlag New York Inc., US, 2005

A propos de cet article

Scientists and others today often collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data.  Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modeling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meteorology, biomechanics, equine science, economics, and medicine.The book presents novel statistical technology, much of it based on the authors' own research work, while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields.  This second edition is aimed at a wider range of readers, and especially those who would like to apply these techniques to their research problems. It complements the authors' other volume Applied Functional Data Analysis: Methods and Case Studies. In particular, there is an extended coverage of data smoothing and other matters arising in the preliminaries to a functional data analysis. The chapters on the functional linear model and modeling of the dynamics of systems through the use of differential equations and principal differential analysis have been completely rewritten and extended to include new developments. Other chapters have been revised substantially, often to give more weight to examples and practical considerations.

N° de réf. du vendeur LU-9780387400808

Détails bibliographiques

• Titre: Functional Data Analysis
• Éditeur: Springer-Verlag New York Inc., US
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 038740080X
• ISBN à 13 chiffres: 9780387400808
• Reliure: Hardback
• Édition: 2nd ed. 2005.
• État de l'article: New

A propos de ce titre

Synopsis

This is the second edition of a highly successful first edition. It contains a considerable amount of new material. Much of the work is original to the authors. Bernard Silverman has been very successful in writing books at a level and a style that appeals to theoretical and applied audiences.

Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis.

The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields.

Présentation de l'éditeur

This is the second edition of a highly succesful book which has sold nearly 3000 copies world wide since its publication in 1997. Many chapters will be rewritten and expanded due to a lot of progress in these areas since the publication of the first edition. Bernard Silverman is the author of two other books, each of which has lifetime sales of more than 4000 copies. He has a great reputation both as a researcher and an author. This is likely to be the bestselling book in the Springer Series in Statistics for a couple of years.