Functional Data Analysis (Springer Series in Statistics) [Hardcover]

Jim Ramsay (Author), B. W. Silverman (Author)

Book Description

Publication Date: June 8, 2005 | Series: Springer Series in Statistics

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.

Editorial Reviews

Review

From the reviews of the second edition: "This book is a second edition of the authors’ 1997 book under the same title. … The new edition is an excellent summary of recent work on FDA, emphasising the aspects of data exploration and data analytic methods that are so far most developed. … The appendices are valuable and helpful. The references (14 pages) are also quite adequate and up to date for readers who have time to explore in more depth. … this book is a good start for a modern statistician." (Z. Q. John Lu, Journal of Applied Statistics, Vol. 33 (6), 2006) "This second edition, more than a third longer, presents a significant expansion. New analytic and graphical tools have been added. Approximate confidence intervals are included. The topics are introduced with more discussion and the examples are described in greater detail. This edition is useful to a broader audience. This is a book for data analysts. … The book is a valuable source of techniques. The author’s software is available. Exploratory graphical methods are uniquely useful in learning from data." (D. F. Andrews, Short Book Reviews, Vol. 25 (3), 2005) "The authors … are leading experts in functional data analysis, and they have provided a comprehensive discussion on various statistical techniques for the analysis of functional data.… The book contains an impressive collection of examples … and those make the book really enjoyable to read. … The presentation is … very lucid, making the book very useful for students and young researchers. I expect the book to be widely read and referenced within the statistical community as well as scientists from different disciplines." (Probal Chaudhri, Sankhya, Vol. 68 (2), 2006) "Functional Data Analysis is well worth reading. A recurring comment is that the motivating examples are compelling and enlightening, and that the level of mathematical and statistical sophistication required to understand the book is kept at the level of an introductory graduate-level course, which makes for pleasant reading." (Mario Peruggia, Journal of the American Statistical Association, Vol. 104 (486), June, 2009)

From the Back Cover

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 recent 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. Jim Ramsay is Professor of Psychology at McGill University and is an international authority on many aspects of multivariate analysis. He was President of the Statistical Society of Canada in 2002-3 and holds the Society’s Gold Medal for his work in functional data analysis. Bernard Silverman is Master of St Peter’s College and Professor of Statistics at Oxford University. He was President of the Institute of Mathematical Statistics in 2000–1. He is a Fellow of the Royal Society. His main specialty is in computational statistics, and he is the author or editor of several highly regarded books in this area.  

Product Details

• Hardcover: 450 pages
• Publisher: Springer; 2nd edition (June 8, 2005)
• Language: English
• ISBN-10: 038740080X
• ISBN-13: 978-0387400808
• Product Dimensions: 9.3 x 6.3 x 1.1 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (6 customer reviews)
• Amazon Best Sellers Rank: #1,189,197 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

36 of 36 people found the following review helpful:

5.0 out of 5 stars First book on an important subject, July 26, 2000

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Random Thoughts (Maryland) - See all my reviews

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This review is from: Functional Data Analysis (Springer Series in Statistics) (Hardcover)

This book deals with statistical analyis of multivariate data which may be treated preferably as curves. Examples of such situations include multivariate time series data which are observed at unequally spaced intervals, and two-way data in social sciences, and many high-dimensional data. Since this is the first attempt at a systematic account of this rapidly growing area, it wisely chooses to focus on descriptive and exploratory techniques developed by the authors and others. The readers are well-advised to have some background on smoothing spline which is employed as the key modeling framework.

For curious readers like me, it still leaves more to be desired. For example, the theory is better prepared by Grenander (1981)'s Abstract Inference, while the practice is preceded by the vast work on analysis of space-time field (4-D var) in climate research using EOF, similar to the principal components, but applied to the 2-d field data. I would also like to see more discussion of alternative modeling techniques such as wavelets and kernel smoothing methods.

I find this book a handy reference, so would recommend to others for the same purpose.

27 of 27 people found the following review helpful:

5.0 out of 5 stars first good treatment of the topic and the theory behind the applications, January 23, 2008

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Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
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This review is from: Functional Data Analysis (Springer Series in Statistics) (Hardcover)

Bernie Silverman is a great writer. Once again along with Ramsay he has written a very accessible book on an interesting but difficult topic. Functional data are series of curves. These kinds of data are often treated under the topic of longitundal data analysis and of course they can also be put under the general category of mutlivariate analysis. Because the x axis often represents time you may also view the analysis of these data as falling in the category of multivariate time series.
Jon Ramsay is a professor of psychology who has contributed to the research in multivariate analysis and has a lot of experience with important applications of functional data analysis. He has had many major publications on this topic in leading statistical journals and has made advances in curve registration and in the development of principal differential analysis.

What is exploited in the functional data analysis approach is the treatment of families of such functions through basis functions (wavelets, Fourier series, orthogonal polynomials etc.). The canonical example is a group of adult males whose growth curves are under study. Each curve has a similar shape but each individual has some differences in the asymptote and other parameters of the curve. Defining these parameters, chosing the approximating functions and assessing the fit to the data are all part of art of functional data analysis.

Silverman is an expert in smoothing and kernal density techniques and you will see his expertise and research contribution exhibited in this text. The roughness penalty approach is one method covered in this book and in more detail in a Chapman and Hall monograph with Green.

Registration of curves is a particular technique that is unique to functional data analysis. Other techniques discussed in the book are generalizations or extensions of existing multivariate techniques such as principal components and canonical correlations.

Shape and smoothness of a curve can be described through derivatives and so differential operators play an important role in functional data analysis. It has a chapter devoted to it and another chapter on a technique called principal differential analysis.

The book concludes with a forward looking chapter on the future of functional data analysis and the challenges that remain ahead.

Also look at the fine review on amazon by dataguru who emphasizes the exploratory aspects of the approach presented in this text and the need to have some knowledge of spline functions.

12 of 12 people found the following review helpful:

5.0 out of 5 stars Nice Book, Powerful tools, Beautiful Subject, May 23, 2000

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William Neely (Madison WI & Seattle WA) - See all my reviews
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This review is from: Functional Data Analysis (Springer Series in Statistics) (Hardcover)

The authors introduce the field of functional data analysis. In a nutshell, they use the techniques of functional analysis (the field of mathematics that deals with spaces of functions and operators) to extend the techniques of multivariate statistics to situations where the data are functional. Silverman and Ramsay present several very well motivated examples that clearly demonstrate the utility of their techniques.

The techniques presented in Functional Data Analysis are potentially very useful to people working in a variety of fields. Ecologist's building dynamical models, engineers trying to classify sensor readings, and statisticians trying to understand how traditional multivariate techniques generalize to functional data can all benefit from this book.

In addition to presenting interesting and usable ideas, the authors' presentation is clear and easily read. This is a very good book!