Methodology and convergence rates for functional linear regression

Details Methodology and convergence rates for functional linear regression Methodology and convergence rates for functional linear regression

2007-08-03

arxiv.org/abs/0708.0466v1

Annals of Statistics 2007, Vol. 35, No. 1, 70-91

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.1214/009053606000000957 in the Annals of Statistics (http://www.imstat.org/aos/) by the Inst

Scientific paper

10.1214/009053606000000957

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.

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