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Functional data analysis
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Springer series in statistics
2nd ed.
Publication Information:
New York, NY : Springer, 2006
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Item Barcode
Call Number
Material Type
Item Category 1
30000010144238 QA278 R35 2006 Open Access Book Book
30000010119737 QA278 R35 2006 Open Access Book Book

On Order



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.

Reviews 1

Choice Review

Samples of curves, or functional data, result from repeated measurements on units, typically observations over time at possibly irregular intervals. Ramsay and Silverman present statistical technology for examining samples of functional data. Their emphasis is on exploratory analyses to reveal new and interesting aspects of the data. Chapters offer introductory materials; discuss smoothing by fitting smooth functions and by applying roughness penalties; and discuss problems of curve registration; i.e., the alignment of common characteristics of a sample of curves. Other chapters examine principal component analyses for functional data; discuss functional analogues of linear models, models with scalar responses, and models with functional responses; explore the functional analogue of canonical correlation; and develop functional methods that exploit derivatives. Techniques are illustrated throughout with data from real applications. A thorough introduction to a collection of tools and techniques in an area of growing importance. Graduate students through professionals. F. Giesbrecht; North Carolina State University

Table of Contents

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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