Functional Data Analysis with R

Reviews / Votes

"The book provides a modern treatment of Functional Data Analysis (FDA). It is written by accomplished experts who have published a large number of influential papers and have first-hand experience of analyzing complex data sets. It will be most useful for researchers and students wishing to focus on practical and correct application of FDA techniques. All methods are explained using nontrivial data sets a practitioner may encounter. Plentiful graphs are provided to facilitate understanding. The book contains useful treatments of subjects not covered in any other textbooks, including survival analysis with functional predictors, multilevel functional data and a self-contained, modern treatment of clustering methods. I highly recommend it to all researchers wishing to master state-of-the-art FDA methods, especially with a view toward applications in health sciences."
~Piotr S. Kokoszka, Professor of Statistics, Colorado State University

"This excellent monograph is firmly attached to the three pillars for progress in data analysis: (1) the mathematical framework, (2) data that are both interesting and abundant, and (3) the challenging task of developing the software that connects the math to the data. The historical material in the preface and bibliography is invaluable for appreciating the great advances in functional data analysis since the 2005 edition of the founding volume. A functional version linear regression analysis is the main focus of; and for a first read I would suggest, after visiting the four data sets in Chapter 1, then skipping to the scalar on function material in Chapter 4 and the function on function applications in Chapter 6. The book is indispensable for researchers and practitioners who work with functions as data objects."
~Jim Ramsay, McGill University

"This book provides a good conceptual understanding of the various tools that are available for the analysis of functional data. At the same time, it is careful not to neglect the practical aspects of performing such analyses in practice. Without sacrificing mathematical rigor, the authors provide a comprehensive overview of the state of the art by taking the readers along with them on a pleasant walk through a series of applications to interesting and relevant data. The examples are accompanied by R code (with ample comments!) and plenty of informative graphical displays throughout. The pace is good and the topics are well organized. This would be a good comprehensive introduction for somebody new to functional data and a good reference for those already working in the area."
~Todd Ogden, Columbia University

"Functional data, i.e., curves and surfaces indexed by continuous parameters, are increasingly common due to technological advances such as accelerometers and blood pressure monitors. This is a noteworthy book by authors who have contributed to the methodology of functional data analysis (FDA) and who have extensive experience with its applications. It is a valuable resource for statisticians, engineers, and data scientists working with functional data and an excellent introduction to R's refund package. The book can serve as an excellent textbook for master's courses in biostatistics and statistics departments. Data scientists will appreciate the numerous examples, many of them new and due to the authors. There are close connections between FDA and nonparametric function estimation, e.g., splines and other smoothing methods, and the book is also a valuable introduction to that topic. Readers should have a background in statistics including regression and multivariate analysis, e.g., principal components."
~David Ruppert, Cornell University "The book provides an extensive account of modern methods of Functional Data Analysis (FDA) particularly well suited for biostatisticians who want to apply its methods to solve specific research problems. Plentiful examples and immediately useable R code are provided. Whilemotivation andmost data examples
come from biomedical applications, the exposition is general, so researchers and students in other fields of science and engineering will find it up to the mark.
~ Piotr S. Kokoszka (26 Nov 2024), Journal of the American Statistical Association