The Annals of Statistics

Estimation of functional derivatives

Peter Hall, Hans-Georg Müller, and Fang Yao

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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

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

First available in Project Euclid: 17 August 2009

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G20: Asymptotic properties

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


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

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