In functional linear regression, the slope “parameter” is a function. Therefore, in a nonparametric context, it is determined by an infinite number of unknowns. Its estimation involves solving an ill-posed problem and has points of contact with a range of methodologies, including statistical smoothing and deconvolution. The standard approach to estimating the slope function is based explicitly on functional principal components analysis and, consequently, on spectral decomposition in terms of eigenvalues and eigenfunctions. We discuss this approach in detail and show that in certain circumstances, optimal convergence rates are achieved by the PCA technique. An alternative approach based on quadratic regularisation is suggested and shown to have advantages from some points of view.
Publié le : 2007-02-14
Classification:
Deconvolution,
dimension reduction,
eigenfunction,
eigenvalue,
linear operator,
minimax optimality,
nonparametric,
principal components analysis,
smoothing,
quadratic regularisation,
62J05,
62G20
@article{1181100181,
author = {Hall, Peter and Horowitz, Joel L.},
title = {Methodology and convergence rates for functional linear regression},
journal = {Ann. Statist.},
volume = {35},
number = {1},
year = {2007},
pages = { 70-91},
language = {en},
url = {http://dml.mathdoc.fr/item/1181100181}
}
Hall, Peter; Horowitz, Joel L. Methodology and convergence rates for functional linear regression. Ann. Statist., Tome 35 (2007) no. 1, pp. 70-91. http://gdmltest.u-ga.fr/item/1181100181/