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Recent Advances on Functional Additive Regression

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Recent Advances in Functional Data Analysis and Related Topics

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

We introduce a flexible approach to approximate the regression function in the case of a functional predictor and a scalar response. Following the Projection Pursuit Regression principle, we derive an additive decomposition which exploits the most interesting projections of the prediction variable to explain the response. The goodness of our procedure is illustrated from theoretical and pratical points of view.

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References

  1. Ferraty, F., Goia, A., Salinelli, E., Vieu, P.: Additive Functional Regression based on Predictive Directions. WP 13/10, Dipartimento di Scienze Economiche e Metodi Quantitativi, Universit`a del Piemonte Orientale A. Avogadro (2010)

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  2. Ferraty, F., Vieu, P.: Nonparametric functional data analysis. Springer, New York (2006) 3. Friedman, J.H., Stuetzle, W.: Projection Pursuit Regression. J. Am. Stat. Assoc. 76, 817–823 (1981)

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Author information

Authors and Affiliations

  1. Institut de Mathématiques de Toulouse, Toulouse, France

    Frédéric Ferraty & Philippe Vieu

  2. Università del Piemonte Orientale, Piemonte, Novara

    Aldo Goia & Enersto Salinelli

Authors
  1. Frédéric Ferraty
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  2. Aldo Goia
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  3. Enersto Salinelli
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  4. Philippe Vieu
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Corresponding author

Correspondence to Frédéric Ferraty .

Editor information

Editors and Affiliations

  1. Toulouse University, Mathematics Toulouse Institute, Narbonne road 118, Toulouse, 31062, France

    Frédéric Ferraty

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© 2011 Springer-Verlag Berlin Heidelberg

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Ferraty, F., Goia, A., Salinelli, E., Vieu, P. (2011). Recent Advances on Functional Additive Regression. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_15

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