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Additive prediction and boosting for functional data

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  • Ferraty, Frédéric
  • Vieu, Philippe

Abstract

Additive model and estimates for regression problems involving functional data are proposed. The impact of the additive methodology for analyzing datasets involving various functional covariates is underlined by comparing its predictive power with those of standard (i.e. non additive) nonparametric functional regression methods. The comparison is made both from a theoretical point of view, and from a real environmental functional dataset. As a by-product, the method is also used for boosting nonparametric functional data analysis even in situations where a single functional covariate is observed. A second functional dataset, coming from spectrometric analysis, illustrates the interest of this functional boosting procedure.

Suggested Citation

  • Ferraty, Frédéric & Vieu, Philippe, 2009. "Additive prediction and boosting for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1400-1413, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1400-1413
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    References listed on IDEAS

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    1. Manteiga, Wenceslao Gonzalez & Vieu, Philippe, 2007. "Statistics for Functional Data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4788-4792, June.
    2. Escabias, M. & Aguilera, A.M. & Valderrama, M.J., 2007. "Functional PLS logit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4891-4902, June.
    3. Chiou, Jeng-Min & Muller, Hans-Georg, 2007. "Diagnostics for functional regression via residual processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4849-4863, June.
    4. Cardot, Herve & Crambes, Christophe & Kneip, Alois & Sarda, Pascal, 2007. "Smoothing splines estimators in functional linear regression with errors-in-variables," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4832-4848, June.
    5. Harezlak, Jaroslaw & Coull, Brent A. & Laird, Nan M. & Magari, Shannon R. & Christiani, David C., 2007. "Penalized solutions to functional regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4911-4925, June.
    6. Aneiros-Pérez, Germán & Vieu, Philippe, 2006. "Semi-functional partial linear regression," Statistics & Probability Letters, Elsevier, vol. 76(11), pages 1102-1110, June.
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