Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416139 | Computational Statistics & Data Analysis | 2007 | 17 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Hervé Cardot, Christophe Crambes, Alois Kneip, Pascal Sarda,