Functional Data Analysis

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.

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)