The Annals of Statistics

Estimation of functional derivatives

Peter Hall, Hans-Georg Müller, and Fang Yao

Full-text: Open access

Abstract

Situations of a functional predictor paired with a scalar response are increasingly encountered in data analysis. Predictors are often appropriately modeled as square integrable smooth random functions. Imposing minimal assumptions on the nature of the functional relationship, we aim to estimate the directional derivatives and gradients of the response with respect to the predictor functions. In statistical applications and data analysis, functional derivatives provide a quantitative measure of the often intricate relationship between changes in predictor trajectories and those in scalar responses. This approach provides a natural extension of classical gradient fields in vector space and provides directions of steepest descent. We suggest a kernel-based method for the nonparametric estimation of functional derivatives that utilizes the decomposition of the random predictor functions into their eigenfunctions. These eigenfunctions define a canonical set of directions into which the gradient field is expanded. The proposed method is shown to lead to asymptotically consistent estimates of functional derivatives and is illustrated in an application to growth curves.

Article information

Source
Ann. Statist., Volume 37, Number 6A (2009), 3307-3329.

Dates
First available in Project Euclid: 17 August 2009

Permanent link to this document
https://projecteuclid.org/euclid.aos/1250515388

Digital Object Identifier
doi:10.1214/09-AOS686

Mathematical Reviews number (MathSciNet)
MR2549561

Zentralblatt MATH identifier
1369.62064

Subjects
Primary: 62G05: Estimation 62G20: Asymptotic properties

Keywords
Consistency functional data analysis gradient field growth curve Karhunen–Loève expansion principal component small ball probability

Citation

Hall, Peter; Müller, Hans-Georg; Yao, Fang. Estimation of functional derivatives. Ann. Statist. 37 (2009), no. 6A, 3307--3329. doi:10.1214/09-AOS686. https://projecteuclid.org/euclid.aos/1250515388


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