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Functional Data Analysis with R and MATLAB (Use R!), by James O. Ramsay, Giles Hooker, Spencer Graves
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The book 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 this book
The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems
- Sales Rank: #976709 in Books
- Published on: 2010-04-02
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .51" w x 6.00" l, .70 pounds
- Binding: Paperback
- 202 pages
Review
From the reviews:
“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)
“The intended audience is anybody performing FDA who must implement or use FDA software. … The goal is to educate and equip the reader for research in and/or implementation of FDA. I strongly recommend the book and will briefly describe each chapter. Most chapters include exercises so the text could easily be used as a text in a course on FDA. … In conclusion, this is a very welcome fda book.” (Tom Burr, Technometrics, Vol. 52 (4), November, 2010)
From the Back Cover
Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems.� It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures.
Jim Ramsay is Professor Emeritus at McGill University and is an international authority on many aspects of multivariate analysis.� He was President of the Statistical Society of Canada in 2002-3 and holds the Society’s Gold Medal for his work in functional data analysis. His statistical work draws on his collaboration with researchers in biomechanics, chemical engineering, climatology, ecology, economics, human biology, medicine and psychology.
Giles Hooker is Assistant Professor of Biological Statistics and Computational Biology at Cornell University. His research interests include statistical inference in nonlinear dynamics, machine learning and computational statistics.
Spencer Graves is an engineer with a PhD in Statistics and over 15 years experience using S-Plus and R to analyze data in a broad range of applications. He has made substantive contributions to several CRAN packages including ‘fda’ and ‘DierckxSpline.’
�Most helpful customer reviews
7 of 7 people found the following review helpful.
Excellent
By Dimitri Shvorob
The book maintains the high standards of "FDA" and "AFDA", and does a great job on the Matlab/R side of things. (Kudos to Spencer Graves). Software on James Ramsay's FTP site includes both the tools and extensive usage examples, which is very helpful. Also available off the site are a number of presentations discussing FDA, which make for valuable complementary reading. I wish the authors built a user-friendly "landing page" / readme file for their code, and filled in more implementation details ("how do I construct a b-spline?"), but admire the book as it is.
0 of 0 people found the following review helpful.
One Star
By Yizhi Sun
No hands-on code example for Matlab
7 of 12 people found the following review helpful.
One of the most poorly written technical books I've seen
By pack
While the concept and practice of Functional Data Analysis is extremely important this book isn't the way to learn it or to learn to use the Matlab implementation. The book is written almost exclusively using R notation with Matlab functions stuffed in as an afterthought. I admit that I'm not a fan of R but I have many years of experience using Matlab. The examples often fail to define locally what the entries mean or even why you would use specific kinds of entries. Aside from the failings of this book all of the books that have anything to do with Ramsay's input have exactly the same examples. This is the worst possible practice if making the concepts clear is the goal. A few additional simple examples different from the main examples would be extremely helpful. If you know or want to use Matlab to do FDA you are better served by writing your own code and ignoring this particular book.
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