Smoothing splines estimators in functional linear regression with errors-in-variables

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The Total Least Squares method is generalized in the context of the functional linear model. A smoothing splines estimator of the functional coeﬃcient of the model is ﬁrst proposed without noise in the covariates and an asymptotic result for this estimator is obtained. Then, this estimator is adapted to the case where the covariates are noisy and an upper bound for the convergence speed is also derived. The estimation procedure is evaluated by means of simulations. Key words Functional Linear Model, Smoothing Splines, Penalization, Errors-in-Variables, Total Least Squares.

Hervé Cardot, Christophe Crambes, Alois Kne

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CSDA 2007 | Functional Linear Model | Smoothing Splines | Smoothing Splines Estimator |

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Added	13 Dec 2010
Updated	13 Dec 2010
Type	Journal
Year	2007
Where	CSDA
Authors	Hervé Cardot, Christophe Crambes, Alois Kneip, Pascal Sarda

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Smoothing splines estimators in functional linear regression with errors-in-variables

CSDA 2007 | Functional Linear Model | Smoothing Splines | Smoothing Splines Estimator |

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