Statistics Papers

Document Type

Journal Article

Date of this Version

2006

Publication Source

The Annals of Statistics

Volume

34

Issue

5

Start Page

2159

Last Page

2179

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.

Keywords

bootstrap, covariance, dimension reduction, eigenfunction, eigenvalue, eigenvector, functional data analysis, intercept, minimax, optimal convergence rate, principal components analysis, rate of convergence, slope, smoothing, spectral decomposition

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Date Posted: 27 November 2017

This document has been peer reviewed.