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Introduction to IWFOS'2008

  • Alain Boudou
  • Frédéric Ferraty
  • Yves Romain
  • Pascal Sarda
  • Philippe Vieu
  • Sylvie Viguier-Pla
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

The working group STAPH is pleased to organize the First International Workshop on Functional and Operational Statistics (IWFOS). After several years of fruitful collaboration and exchange with national and international experts in the field “Statistics in infinite dimensional spaces”, the need for such a workshop was becoming increasingly evident. The workshop will ofier participants an overview of the current state of knowledge in this area, whilst at the same time providing them with an opportunity to share their own experience.

Keywords

Functional Data Dimensional Setting Functional Data Analysis Fruitful Collaboration High Dimensional Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Benchikh, T. Boudou, A. Romain, Y.: Mesures aleatoires operatorielle et banachique. Application aux series stationnaires. (French) [Operatorial and Banach space-valued random measures. Application to stationary series] C. R. Math. Acad. Sci. Paris, 345 (6), 345-348 (2007).MATHMathSciNetGoogle Scholar
  2. [2]
    Bosq, D.: Linear processes in function spaces, theory and applications. Lecture notes in statistics, 149, Springer-Verlag, New York. (2000).MATHGoogle Scholar
  3. [3]
    Boudou, A., Viguier-Pla, S.: On proximity between PCA in the frequency domain and usual PCA. Statistics, 40, 447-464. (2006).MATHMathSciNetGoogle Scholar
  4. [4]
    Crambes, C., Kneip, A., Sarda, P.: Smoothing splines estimators for functional linear regression. Ann. Statist. (to appear). (2008).Google Scholar
  5. [5]
    Collomb, G.: Méthodes non paramétriques en régression, analyse de séries tem-porelles, prédiction et discrimination. Univesité Paul Sabatier, Thèse d'Etat. (1983).Google Scholar
  6. [6]
    De Boor, C.: A practical guide to splines. Springer, New York. (1978).MATHGoogle Scholar
  7. [7]
    De Leeuw, J. and J. Van Rijckvorsel.: Component and Correspondence analysis: dimension reduction by functional approximation. Wiley, New York. (1988).Google Scholar
  8. [8]
    Dauxois, J. and Pousse, A.: Une extension de l'analyse canonique. Quelques appli-cations. Annales de l'Institut Henri Poincare, Vol. XI , 4, 355-379. (1975).MathSciNetGoogle Scholar
  9. [9]
    Ferraty, F., Vieu, P.: Nonparametric functional data analysis: theory and practice. Springer, New York. (2006).MATHGoogle Scholar
  10. [10]
    Hastie, T. and Tibshirani, R. J.: Generalized additive models. Chapman and Hall, New York. (1990).MATHGoogle Scholar
  11. [11]
  12. [12]
    Ramsay, J. and Silverman, B.: Functional data analysis. Springer, New York. (1997).MATHGoogle Scholar
  13. [13]
    Romain, Y.: Perturbation of functional tensors with applications to covariance op-erators. Stat. Proba. Letters, 58, 253-264 (2002).MATHCrossRefMathSciNetGoogle Scholar
  14. [14]

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Alain Boudou
    • 1
  • Frédéric Ferraty
    • 1
  • Yves Romain
    • 1
  • Pascal Sarda
    • 1
  • Philippe Vieu
    • 1
  • Sylvie Viguier-Pla
    • 1
  1. 1.Institut de Mathématiques de Toulouse Equipe LSPUniversité Paul SabatierFrance

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