Functional data analysis

Jim Ramsay, Bernard Silverman - Collection Springer Series In Statistics

310 pages, parution le 20/04/2004

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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. Jim Ramsay is Professor of Psychology at McGill University and is an international authority on many aspects of multivariate analysis. He draws on his collaboration with researchers in speech articulation, motor control, meteorology, psychology, and human physiology to illustrate his technical contributions to functional data analysis in a wide range of statistical and application journals. Bernard Silverman, author of the highly regarded "Density Estimation for Statistics and Data Analysis", and coauthor of "Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach," is Professor of Statistics at Bristol University. His published work on smoothing methods and other aspects of applied, computational, and theoretical statistics has been recognized by the Presidents' Award of the Committee of Presidents of Statistical Societies, and the award of two Guy Medals by the Royal Statistical Society.

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

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	20/04/2004
Nb. de pages	310
Format	16 x 24
Couverture	Relié
Poids	600g
Intérieur	Noir et Blanc
EAN13	9780387949567
ISBN13	978-0-387-94956-7