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Methodology and convergence rates for functional linear regression. (English) Zbl 1114.62048
Summary: 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.

MSC:
62G08 Nonparametric regression and quantile regression
62H25 Factor analysis and principal components; correspondence analysis
62G20 Asymptotic properties of nonparametric inference
62J05 Linear regression; mixed models
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