The Oxford handbook of functional data analysis
By: edited by F. Ferraty, Y. Romain
Publisher: Oxford University Press, USA
Published: 2011
Place of publication: Oxford; New York
Language: eng
Number of pages: 494
ISBN-13: 9780199568444
Description: "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 to observe 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 of this 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 the same 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"--
Table of contents: title: Machine generated contents note:; title: List of illustrations; title: List of datasets; title: <strong>PART I: REGRESSION MODELLING FOR FDA </strong>; title: 1. Unifying presentation for functional regression modelling, F. Ferraty and P. Vieu; title: 2. Functional linear regression, H. Cardot and P. Sarda; title: 3. Linear processes for functional data, A. Mas and B. Pumo; title: 4. Kernel regression estimation for functional data, F. Ferraty and P. Vieu; title: 5. Nonparametric methods for alpha-mixing functional data, L. Delsol; title: 6. Functional coefficient models for economics and financial data, Z. Cai; title: <strong>PART II: BENCHMARK METHODS FOR FDA </strong>; title: 7. Resampling methods for functional data, T. McMurry and D. Politis; title: 8. Functional principal component analysis, P. Hall; title: 9. Curve registration, J. Ramsay; title: 10. Classification methods for functional data, A. Baillo, A. Cuevas, and R. Fraiman; title: 11. Sparse functional data analysis, G. James; title: <strong>PART III: TOWARDS STOCHASTIC BACKGROUND IN INFINITE-DIMENSIONAL SPACES </strong>; title: 12. Vector integration in Banach spaces, N. Dinculeanu; title: 13. Operator geometry in Statistics, K. Gustafson; title: 14. On Bernstein type and maximal inequalities for dependent Banach-valued random vectors and applications, N. Rhomari; title: 15. On spectral and random measures associated to a stationary process, A. Boudou and Y. Romain; title: 16. An invitation to operator-based Statistics, Y. Romain; title: Index.