The Oxford Handbook of Functional Data Analysis

Hardcover | December 25, 2010

EditorFrederic Ferraty, Yves Romain

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As technology progresses, we are able to handle larger and larger datasets. At the same time, monitoring devices such as electronic equipment and sensors (for registering images, temperature, etc.) have become more and more sophisticated. This high-tech revolution offers the opportunity toobserve phenomena in an increasingly accurate way by producing statistical units sampled over a finer and finer grid, with the measurement points so close that the data can be considered as observations varying over a continuum. Such continuous (or functional) data may occur in biomechanics (e.g.human movements), chemometrics (e.g. spectrometric curves), econometrics (e.g. the stock market index), geophysics (e.g. spatio-temporal events such as El Nino or time series of satellite images), or medicine (electro-cardiograms/electro-encephalograms).It is well known that standard multivariate statistical analyses fail with functional data. However, the great potential for applications has encouraged new methodologies able to extract relevant information from functional datasets. This Handbook aims to present a state of the art exploration ofthis high-tech field, by gathering together most of major advances in this area. Leading international experts have contributed to this volume with each chapter giving the key original ideas and comprehensive bibliographical information. The main statistical topics (classification, inference,factor-based analysis, regression modelling, resampling methods, time series, random processes) are covered in the setting of functional data. The twin challenges of the subject are the practical issues of implementing new methodologies and the theoretical techniques needed to expand the mathematical foundations and toolbox. The volume therefore mixes practical, methodological and theoretical aspects of the subject, sometimes within thesame chapter. As a consequence, this book should appeal to a wide audience of engineers, practitioners and graduate students, as well as academic researchers, not only in statistics and probability but also in the numerous related application areas.

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As technology progresses, we are able to handle larger and larger datasets. At the same time, monitoring devices such as electronic equipment and sensors (for registering images, temperature, etc.) have become more and more sophisticated. This high-tech revolution offers the opportunity toobserve phenomena in an increasingly accurate w...

Frederic Ferraty is a researcher in Statistics at Toulouse University (France). He has been working on all facets of Statistics, ranging from fundamental theory basis, methodology developments to practical implementation. In addition, most of the major topics of Statistics as Classification, Exploratory Methods, Regression, Time Series...

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Format:HardcoverDimensions:512 pagesPublished:December 25, 2010Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199568448

ISBN - 13:9780199568444

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Table of Contents

List of illustrationsList of datasetsPart I: Regression Modelling for FDA1. F. Ferraty and P. Vieu: Unifying presentation for functional regression modelling2. H. Cardot and P. Sarda: Functional linear regression3. A. Mas and B. Pumo: Linear processes for functional data4. F. Ferraty and P. Vieu: Kernel regression estimation for functional data5. L. Delsol: Nonparametric methods for alpha-mixing functional data6. Z. Cai: Functional coefficient models for economics and financial dataPart II: Benchmark Methods for FDA7. T. McMurry and D. Politis: Resampling methods for functional data8. P. Hall: Functional principal component analysis9. J. Ramsay: Curve registration10. A. Baillo, A. Cuevas, and R. Fraiman: Classification methods for functional data11. G. James: Sparse functional data analysisPart III: Towards Stochastic Background in Infinite-Dimensional Spaces12. N. Dinculeanu: Vector integration in Banach spaces13. K. Gustafson: Operator geometry in Statistics14. N. Rhomari: On Bernstein type and maximal inequalities for dependent Banach-valued random vectors and applications15. A. Boudou and Y. Romain: On spectral and random measures associated to a stationary process16. Y. Romain: An invitation to operator-based StatisticsIndex