Functional Data Analysis with R and MATLAB provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in Functional Data Analysis with R and MATLAB The code in R and Matlab in Functional Data Analysis with R and MATLAB has been designed to permit easy modification to adapt to new data structures and research problems
"The book is intended as a means of introducing functional data analysis to those who would like to use it as a research tool in a variety of applications. It gives a brief but clear description of the concepts and methods together with a strong focus on implementation. The mixture of R and RATLAB illustrative code works well and the latter computing environment, together with the material on dynamics, will suit those from an engineering or physical sciences background. It therefore provides an excellent starting point for those who would like to make use of these very powerful techniques in analyzing data."
- Journal of Statistical Software, April 2010, Vol. 34, Book Review 3
"This well-written book provides a great, intuitive introduction to functional data analysis [...] . I recommend this book for statisticians wanting to learn about the basics of functional data analysis, as well as practitioners wanting to explore their own data and perform some analyses on their own. [...] it would be a good basis for an applied course in functional data analysis that could be taken by statistics and biostatistics M.S. and Ph.D. students as well as other scientists with a reasonably deep quantitative background."
- Jeffrey S. Morris, The American Statistician, Vol. 65 (4), November, 2011
- Introduction to functional data analysis
- Essential comparisons of the Matlab and R languages
- How to specify basis systems for building functions
- How to build functional data objects
- Smoothing: Computing curves from noisy data
- Descriptions of functional data
- Exploring variation: Functional principal and canonical components analysis
- Registration: Aligning features for samples of curves
- Functional linear models for scalar responses
- Linear models for functional responses
- Functional models and dynamics
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