Functional Data Analysis

Jim Ramsay, Bernard Silverman - Collection Springer Series In Statistics

436 pages, parution le 12/07/2005 (2^eme édition)

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Résumé

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. 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 drwan from growth analysis, meterology, biomechanics, equine science, economics, and medicine.

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. Much of the material is based on the authors' own work, some of which appears here for the first time.

Written for: Researchers, graduate students

L'auteur - Jim Ramsay

Ramsay is Professor of Psychology at McGill University, and is an international authority on many aspects of multivariate analysis. He was elected President of the Statistical Society of Canada for the term 2002-3 and is a holder of the Society's Gold Medal for his work in functional data analysis. His statistical work draws on his collaborations with researchers in speech articulation, biomechanics,

L'auteur - Bernard Silverman

Silverman, B.W., University of Bristol, UK

Sommaire

Introduction
Notation and Techniques
Representing Functional Data as Smooth Functions
The Roughness Penalty Approach
The Registration and Display of Functional Data
Principal Components Analysis for Functional Data
Regularized Principal Components Analysis
Principal Components Analysis of Mixed Data
Functional Linear Models
Functional Linear Models for Scalar Responses
Functional Linear Modesl for Functional Responses
Canonical Correlation and Discriminant Analysis
Differential Operators in Functional Data Analysis
Principal Differential Analysis
More General Roughness Penalties
Some Perspectives on FDA

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Jim Ramsay, Bernard Silverman
Collection	Springer Series In Statistics
Parution	12/07/2005
Édition	2^eme édition
Nb. de pages	436
Format	16 x 24
Couverture	Relié
Poids	770g
Intérieur	Noir et Blanc
EAN13	9780387400808
ISBN13	978-0-387-40080-8