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June 2011 Single and multiple index functional regression models with nonparametric link
Dong Chen, Peter Hall, Hans-Georg Müller
Ann. Statist. 39(3): 1720-1747 (June 2011). DOI: 10.1214/11-AOS882

Abstract

Fully nonparametric methods for regression from functional data have poor accuracy from a statistical viewpoint, reflecting the fact that their convergence rates are slower than nonparametric rates for the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dimension, but nevertheless imposes structural constraints on the relationship between predictors and responses. Recent advances have extended the linear approach by using it in conjunction with link functions, and by considering multiple indices, but the flexibility of this technique is still limited. For example, the link may be modeled parametrically or on a grid only, or may be constrained by an assumption such as monotonicity; multiple indices have been modeled by making finite-dimensional assumptions. In this paper we introduce a new technique for estimating the link function nonparametrically, and we suggest an approach to multi-index modeling using adaptively defined linear projections of functional data. We show that our methods enable prediction with polynomial convergence rates. The finite sample performance of our methods is studied in simulations, and is illustrated by an application to a functional regression problem.

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Dong Chen. Peter Hall. Hans-Georg Müller. "Single and multiple index functional regression models with nonparametric link." Ann. Statist. 39 (3) 1720 - 1747, June 2011. https://doi.org/10.1214/11-AOS882

Information

Published: June 2011
First available in Project Euclid: 25 July 2011

zbMATH: 1220.62040
MathSciNet: MR2850218
Digital Object Identifier: 10.1214/11-AOS882

Subjects:
Primary: 62G05 , 62G08

Keywords: Functional data analysis , generalized functional linear model , prediction , smoothing

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.39 • No. 3 • June 2011
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