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The Oxford handbook of functional data analysis

Fr ed eric Ferraty; Y Romain (Yves)

Oxford Oxford University Press New York 2011

Emprestado de IME - Inst. Matemática e Estatística    (QA278.C3 F379o )(Acessar)

  • Título:
    The Oxford handbook of functional data analysis
  • Autor: Fr ed eric Ferraty; Y Romain (Yves)
  • Assuntos: Multivariate analysis; Statistical functionals; ANÁLISE MULTIVARIADA; ANÁLISE DE DADOS; ANÁLISE FUNCIONAL; COLETÂNEA
  • Notas: Includes bibliographical references and index
  • Descrição: Regression modeling for FDA: A unifying classification for functional regression modeling / Functional linear regression / Linear processes for functional data / Kernel regression estimation for functional data / Nonparametric methods for a(alpha)-mixing functional data / Functional coefficient models for economics and financial data / Benchmark methods for FDA: Resampling methods for functional data / Principal component analysis for functional data: methodology, theory, and discussion / Curve registration / Classification methods for functional data / Sparseness and functional data analysis / Towards a stochastic background in infinite-dimensional spaces: Vector integration and stochastic integration in Banach spaces / Operator geometry in statistics / On Bernstein type and maximal inequalities for dependent Banach-valued random vectors and applications / On product measures associated with stationary processes / An invitation to operator-based statistics /
    "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 gra
  • Títulos relacionados: Série:Oxford handbooks
  • Editor: Oxford Oxford University Press New York
  • Data de criação/publicação: 2011
  • Formato: xvi, 494 p ill 26 cm.
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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