Cai, T. Tony; Hall, Peter Prediction in functional linear regression. (English) Zbl 1106.62036 Ann. Stat. 34, No. 5, 2159-2179 (2006). Summary: There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case of more conventional finite-dimensional regression, much of the practical interest in the slope centers on its application for the purpose of prediction, rather than on its significance in its own right. We show that the problems of slope-function estimation, and of prediction from an estimator of the slope function, have very different characteristics. While the former is intrinsically nonparametric, the latter can be either nonparametric or semiparameric.In particular, the optimal mean-square convergence rate of predictors is \(n^{-1}\), where \(n\) denotes sample size, if the predict and is a sufficiently smooth function. In other cases, convergence occurs at a polynomial rate that is strictly slower than \(n^{-1}\). At the boundary between these two regimes, the mean-square convergence rate is less than \(n^{-1}\) by only a logarithmic factor. More generally, the rate of convergence of the predicted value of the mean response in the regression model, given a particular value of the explanatory variable, is determined by a subtle interaction among the smoothness of the predictand, of the slope function in the model, and of the autocovariance function for the distribution of explanatory variables. Cited in 278 Documents MSC: 62G08 Nonparametric regression and quantile regression 62J05 Linear regression; mixed models 62G20 Asymptotic properties of nonparametric inference Keywords:bootstrap; covariance; dimension reduction; eigenfunction; eigenvalue; eigenvector; intercept; minimax; optimal convergence rate; principal components analysis; rate of convergence; smoothing; spectral decomposition; simulations; slope Software:fda (R) × Cite Format Result Cite Review PDF Full Text: DOI arXiv References: [1] Besse, P. and Ramsay, J. O. (1986). Principal components analysis of sampled functions. Psychometrika 51 285–311. · Zbl 0623.62048 · doi:10.1007/BF02293986 [2] Bhatia, R., Davis, C. and McIntosh, A. (1983). Perturbation of spectral subspaces and solution of linear operator equations. Linear Algebra Appl. 52 / 53 45–67. · Zbl 0518.47013 · doi:10.1016/0024-3795(83)80007-X [3] Boente, G. and Fraiman, R. (2000). Kernel-based functional principal components. Statist. Probab. 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