Functional Data Analysis (Springer Series in Statistics)

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.


first good treatment of the topic and the theory behind the applications   January 23, 2008
Michael R. Chernick (Holland PA)
26 out of 26 found this review helpful

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.




nice introduction to functional data analysis   April 10, 2002
Michael R. Chernick (Malvern, PA)
10 out of 10 found this review helpful

Bernie Silverman is a great writer. Once again 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.


Nice Book, Powerful tools, Beautiful Subject   May 23, 2000
William Neely (Madison WI & Seattle WA)
9 out of 9 found this review helpful

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!


Very clear introduction to functional data analysis   August 16, 2006
Theodore Perkins (McGill University, Montreal, Quebec, Canada)
1 out of 1 found this review helpful

Functional data analysis is a topic of increasing interest in the statistics community, and is most commonly applied to time-series and/or spatial-series data. The main idea is to begin one's analysis of the data by constructing a smoothed or interpolated version, and then do many of the standard statistical things (such as finding principle components or doing regressions) in that smoothed function space.

The book explains the ideas and methods behind functional data analysis very clearly, with a minimum of math and notation and with a number of recurring, illustrative examples. For those interested in quickly getting oriented to the basic concepts and perhaps trying them out, I found that a few days with this book is a good place to start. The authors have also published a book with more detailed applications worked out called "Applied Functional Data Analysis".