Prediction in functional linear regression

• 成果类型: Article
• 署名作者: Cai, T. Tony; Hall, Peter
• 署名单位: University of Pennsylvania; Australian National University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000830
• 发表日期: 2006
• 页码: 2159-2179
• 关键词: principal-components-analysis; logistic-regression; models; CURVES

摘要

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 semi-parametric. 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.