| Applied Functional Data Analysis: Methods and Case Studies 2002 Edition Contributor(s): Ramsay, J. O. (Author), Silverman, B. W. (Author) |
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ISBN: 0387954147 ISBN-13: 9780387954141 Publisher: Springer
Binding Type: Paperback - See All Available Formats & Editions Published: June 2002 Annotation: What do juggling, old bones, criminal careers and human growth patterns have in common? They all give rise to functional data, that come in the form of curves or functions rather than the numbers, or vectors of numbers, that are considered in conventional statistics. The authors' highly acclaimed book Functional Data Analysis (1997) presented a thematic approach to the statistical analysis of such data. By contrast, the present book introduces and explores the ideas of functional data analysis by the consideration of a number of case studies, many of them presented for the first time. The two books are complementary but neither is a prerequisite for the other. The case studies are accessible to research workers in a wide range of disciplines. Every reader, whether experienced researcher or graduate student, should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. Some of the studies demand the development of novel aspects of the methodology of functional data analysis, but technical details aimed at the specialist statistician are confined to sections which the more general reader can safely omit. There is an associated web site with MATLAB and S-PLUS implementations of the methods discussed, together with all the data sets that are not proprietary. Jim Ramsay is Professor of Psychology at McGill University, and is an international authority on many aspects of multivariate analysis. He was elected President of the Statistical Society of Canada for the term 2002-3 and is a holder of the Society's Gold Medal for his work in functional data analysis. His statistical work drawson his collaborations with researchers in speech articulation, biomechanics, economics, human biology, meteorology and psychology. Bernard Silverman is Professor of Statistics at Bristol University. He was President of the Institute of Mathematical Statistics in 2000-1 and has held various offices in the Royal Statistical Society. He is a Fellow of the Royal Society and a member of Academia Europaea. His main specialty is computational statistics, and he is the author or editor of several highly regarded books in this area. He has also published widely in theoretical and applied statistics, and in many other fields, including law, human and veterinary medicine, earth sciences and engineering. Click for more in this series: Springer Series in Statistics |
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| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis |
| Dewey: 519.535 |
| LCCN: 2002022924 |
| Series: Springer Series in Statistics |
| Physical Information: 0.47" H x 6.82" W x 8.78" L (0.81 lbs) 191 pages |
| Features: Bibliography, Illustrated, Index |
| Descriptions, Reviews, Etc. |
| Publisher Description: Almost as soon as we had completed our previous book Functional Data Analysis in 1997, it became clear that potential interest in the ?eld was far wider than the audience for the thematic presentation we had given there. At the same time, both of us rapidly became involved in relevant new research involving many colleagues in ?elds outside statistics. This book treats the ?eld in a di?erent way, by considering case st- ies arising from our own collaborative research to illustrate how functional data analysis ideas work out in practice in a diverse range of subject areas. These include criminology, economics, archaeology, rheumatology, psych- ogy, neurophysiology, auxology (the study of human growth), meteorology, biomechanics, and education--and also a study of a juggling statistician. Obviously such an approach will not cover the ?eld exhaustively, and in any case functional data analysis is not a hard-edged closed system of thought. Nevertheless we have tried to give a ?avor of the range of meth- ology we ourselves have considered. We hope that our personal experience, including the fun we had working on these projects, will inspire others to extend "functional" thinking to many other statistical contexts. Of course, manyofourcasestudiesrequireddevelopmentofexistingmethodology, and readersshouldgaintheabilitytoadaptmethodstotheirownproblemstoo. |
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