Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010144238 | QA278 R35 2006 | Open Access Book | Book | Searching... |
Searching... | 30000010119737 | QA278 R35 2006 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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
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 |