Functional Data Analysis (Springer Series in Statistics) 2nd Edition by James Ramsay (PDF)

Description

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐FDA is a very important new topic in statistics and Ramsay and Silverman provide an accessible introduction to the topic.Functional data occur when the data are curves. For instance, we might monitor growth of children sampled at a fairly fine grid over several years. Or we might consider reports of experienced pain in many patients over a fairly long period of time. Even when the data *seem* discrete (and given measurement error and a finite sampling rate all data really *are* discrete) there may be substantial advantages to treat them as continuous.Functional analysis extends the notion of linear space that is the foundation of statistics to the infinite dimensional case. In a infinite dimensional space, a matrix equation becomes an integral equation, and so on. They provide a useful introduction to the topic, enough that a non-specialist can get into it. The big difference between this treatment and older ones is that Ramsay and Silverman emphasize that the data generating process is assumed to be continuous. Many older treatments of similar data involve no curve regularization or smoothing. Basically they ignore the underlying continuity. Ramsay and Silverman show there are substantial benefits to paying attention to the continuity. For instance, if we want to estimate the derivative of a sampled curve it’s logical to use first differences. They demonstrate, however, that fitting a smooth to the curve, e.g., a spline, and then finding the derivative of the smooth curve often does a much better job. (Why? Differencing amplifies noise.)Anyway, they cover topics of linear models, principal components, canonical correlation, and principal differential analysis in function spaces. Their general feel is fairly exploratory. The one thing this book is short of is long examples, which can be found in their companion volume Applied Functional Data Analysis.

⭐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.

⭐This is a must-have book if your research involves functional data analysis. I am working on my dissertation and I felt like I need to have the hard copy of this book. This book covers all the classical techniques of functional data analysis which is very 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!

⭐I am just going to point out some features that I find relevant when buying a book of this kind:- Clear notation: there is really no way of getting confused. Notation is defined at the beginning of the book and is kept consistent throughout the whole book.- Language: simple, clear and precise. While being strict in the mathematics and inherent technicalities, the book reads very easily.- Examples: many examples are presented in the book. Different fields of applications are included.- Continuity: the connection between the different topics and chapters of the book is very clear. Relating the contents of the different chapters comes very naturally.- Appendix: includes further explanations on selected topics that are not necessarily needed to understand the book but that might be of interest for certain readers.- Further readings: At the end of each chapter the authors propose a few references to further expand your knowledge in certain topics.If you are interested in learning Functional Data Analysis, this book is definitely a good place to start.

⭐The book arrived fast, and has a very good quality. Totally satisfied!

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