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October 2006 Prediction in functional linear regression
T. Tony Cai, Peter Hall
Ann. Statist. 34(5): 2159-2179 (October 2006). DOI: 10.1214/009053606000000830

Abstract

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 semiparametric. In particular, the optimal mean-square convergence rate of predictors is n−1, where n denotes sample size, if the predictand 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.

Citation

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T. Tony Cai. Peter Hall. "Prediction in functional linear regression." Ann. Statist. 34 (5) 2159 - 2179, October 2006. https://doi.org/10.1214/009053606000000830

Information

Published: October 2006
First available in Project Euclid: 23 January 2007

zbMATH: 1106.62036
MathSciNet: MR2291496
Digital Object Identifier: 10.1214/009053606000000830

Subjects:
Primary: 62J05
Secondary: 62G20

Rights: Copyright © 2006 Institute of Mathematical Statistics

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Vol.34 • No. 5 • October 2006
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